Interview with a Data Scientist: Brad Klingenberg

Standard

Bio

Brad Klingenberg is the Director of Styling Algorithms at Stitch Fix in San Francisco. His team uses data and algorithms to improve the selection of merchandise sent to clients. Prior to joining Stitch Fix Brad worked with data and predictive analytics at financial and technology companies. He studied applied mathematics at the University of Colorado at Boulder and earned his PhD in Statistics at Stanford University in 2012.


 

1. What project have you worked on do you wish you could go back to, and do better?

 

Nearly everything! A common theme would be not taking the framing of a problem for granted. Even seemingly basic questions like how to measure success can have subtleties. As a concrete example, I work at Stitch Fix, an online personal styling service for women. One of the problems that we study is predicting the probability that a client will love an item that we select and send to her. I have definitely tricked myself in the past by trying to optimize a measure of prediction error like AUC.

This is trickier than it seems because there are some sources of variance that are not useful for making recommendations. For example, if I can predict the marginal probability that a given client will love any item then that model may give me a great AUC when making predictions over many clients, because some clients may be more likely to love things than others and the model will capture this. But if the model has no other information it will be useless for making recommendations because it doesn’t even depend on the item. Despite its AUC, such a model is therefore useless for ranking items for a given client. It is important to think carefully about what you are really measuring.


 

2. What advice do you have to younger analytics professionals and in particular PhD students in the Sciences and Social Sciences?

 

Focus on learning the basic tools of applied statistics. It can be tempting to assume that more complicated means better, but you will be well-served by investing time in learning workhorse tools like basic inference, model selection and linear models with their modern extensions. It is very important to be practical. Start with simple things.

Learn enough computer science and software engineering to be able to get things done. Some tools and best practices from engineering, like careful version control, go a long ways. Try to write clean, reusable code. Popular tools in R and Python are great for starting to work with data. Learn about convex optimization so you can fit your own models when you need to – it’s extremely useful to be able to cast statistical estimates as the solution to optimization problems.

Finally, try to get experience framing problems. Talk with colleagues about problems they are solving. What tools did they choose? Why? How should did they measure success? Being comfortable with ambiguity and successfully framing problems is a great way to differentiate yourself. You will get better with experience – try to seek out opportunities.


 

3. What do you wish you knew earlier about being a data scientist?

 

I have always had trouble identifying as a data scientist – almost everything I do with data can be considered applied statistics or (very) basic software engineering. When starting my career I was worried that there must be something more to it – surely, there had to be some magic that I was missing. There’s not. There is no magic. A great majority of what an effective data scientist does comes back to the basic elements of looking at data, framing problems, and designing experiments. Very often the most important part is framing problems and choosing a reasonable model so that you can estimate its parameters or make inferences about them.


 

4. How do you respond when you hear the phrase ‘big data’?

 

I tend to lose interest. It’s a very over-used phrase. Perhaps more importantly I find it to be a poor proxy for problems that are interesting. It can be true that big data brings engineering challenges, but data science is generally made more interesting by having data with high information content rather than by sheer scale. Having lots of data does not necessarily mean that there are interesting questions to answer or that those answers will be important to your business or application. That said, there are some applications like computer vision where it can be important to have a very large amount of data.


 

5. What is the most exciting thing about your field?

 

While “big data” is overhyped, a positive side effect has been an increased awareness of the benefits of learning from data, especially in tech companies. The range of opportunities for data scientists today is very exciting. The abundance of opportunities makes it easier to be picky and to find the problems you are most excited to work on. An important aspect of this is to look in places you might not expect. I work at Stitch Fix, an online personal styling service for women. I never imagined working in women’s apparel, but due to the many interesting problems I get to work on it has been the most exciting work of my career.


 

6. How do you go about framing a data problem – in particular, how do you avoid spending too long, how do you manage expectations etc. How do you know what is good enough?

 

As I mentioned previously, it can be helpful to start framing a problem by thinking about how you would measure success. This will often help you figure out what to focus on. You will also seldom go wrong by starting simple. Even if you eventually find that another approach is more effective a simple model can be a hugely helpful benchmark. This will also help you understand how well you can reasonably expect your ultimate approach to perform. In industry, it is not uncommon to find problems where (1) it is just not worth the effort to do more than something simple, or (2) no plausible method will do well enough to be considered successful. Of course, measuring these trade-offs depends on the context of your problem, but a quick pass with a simple model can often help you make an assessment.


 

7. How do you explain to C-level execs the importance of Data Science? How do you deal with the ‘educated selling’ parts of the job? In particular – how does this differ from sports and industry?

 

It is usually better if you are not the first to evangelize the use of data. That said, data scientists will be most successful if they put themselves in situations where they have value to offer a business. Not all problems that are statistically interesting are important to a business. If you can deliver insights, products or predictions that have the potential to help the business then people will usually listen. Of course this is most effective when the data scientist clearly articulates the problem they are solving and what its impact will be.

The perceived importance of data science is also a critical aspect of choosing where to work – you should ask yourself if the company values what you will be working on and whether data science can really make it better. If this is the case then things will be much easier.


 

8. What is the most exciting thing you’ve been working on lately and tell us a bit about it.

 

I lead the styling algorithms team at Stitch Fix. Among the problems we work on is making recommendations to our stylists, human experts who curate our recommendations for our clients. Making recommendations with humans in the loop is fascinating problem because it introduces an extra layer of feedback – the selections made by our stylists. Combining this feedback with direct feedback from our clients to make better recommendations is an interesting and challenging problem.


 

9. What is the biggest challenge of leading a data science team?

 

Hiring and growing a team are constant challenges, not least because there is not much consensus around what data science even is. In my experience a successful data science team needs people with a variety of skills. Hiring people with a command of applied statistics fundamentals is a key element, but having enough engineering experience and domain knowledge can also be important. At Stitch Fix we are fortunate to partner with a very strong data platform team, and this enables us to handle the engineering work that comes with taking on ever more ambitious problems.

Advertisements

Interview with a Data Scientist: Alice Zheng

Standard
I recently caught up with Alice Zheng a Director of Data Science at Dato – Alice is an expert on building scalable Machine Learning models and currently works for www.dato.com who are a company providing tooling to help you build scalable machine learning models easily. She is also a keen advocate of encouraging women in Machine Learning and Computer Science. Alice has a PhD from UC Berkeley and spent some of her post docs at Microsoft Research in Redmond. She is currently based in Washington State in the US.

1. What project have you worked on do you wish you could go back to, and do better?
Too many! The top of the list is probably my PhD thesis. I collaborated with folks in software engineering research and we proposed a new way of using statistics to debug software. They instrumented programs to spit out logs for each run that provide statistics on the state of various program variables. I came up with an algorithm to cluster the failed runs and the variables. The algorithm identifies variables that are most correlated with each subset of failures. Those variables, in turn, can take the programmer very close to the location of the bug in the code.
It was a really fun project. But I’m not happy with the way that I solved the problem. For one thing, the algorithm that I came up with had no theoretical guarantees. I did not appreciate theory when I was younger. But nowadays, I’m starting to feel bad about the lack of rigor in my own work. It’s too easy in machine learning to come up with something that seems to work, maybe even have an intuitive explanation for why it makes sense, and yet not be able to write down a mathematical formula for what the algorithm is actually doing.
Another thing that I wish I had learned earlier is to respect the data more. In machine learning research, the emphasis is on new algorithms and models. But solving real data science problems require having the right data, developing the right features, and finally using the right model. Most of the time, new algorithms and methods are not needed. But a combination of data, features, and model is the key. I wish I’d realized this earlier and spent less time focusing on just one aspect of the whole pipeline.

2. What advice do you have to younger analytics professionals and in particular PhD students in the Sciences?
Be curious. Go deep. And study the arts.
Being curious gives you breadth. Knowing about other fields pulls you out of a narrow mindset focused on just one area of study. Your work will be more inspired, because you are drawing upon diverse sources of information.
Going deep into a subject gives you depth and expertise, so that you can make the right choices when trying to solve a problem, and so that you might more adequately assess the pros and cons of each approach.
Why study the arts? Well, if I had my druthers, art, music, literature, mathematics, statistics, and computer science would be required courses for K12. They offer completely different ways of understanding the world. They are complementary of each other. Knowing more than one way to see the world makes us more whole as human beings. Science _is_ an art form. Analytics is about problem solving, and it requires a lot of creativity and inspiration. It’s art in a different form.

3. What do you wish you knew earlier about being a data scientist?
Hmm, probably just what I said above–respect the data. Look at it in all different ways. Understand what it means. Data is the first class citizen. Algorithms and models are just helpers. Also, tools are important. Finding and learning to use good tools will save a lot of time down the line.

4. How do you respond when you hear the phrase ‘big data’?
Cringe? Although these days I’ve become de-sensitized. 🙂
I think a common misconception about “big data” is that, while the total amount of data maybe big, the amount of _useful_ data is very small in comparison. People might have a lot of data that has nothing to do with the questions they want to answer. After the initial stages of data cleaning and pruning, the data often becomes much much smaller. Not big at all.

5. What is the most exciting thing about your field?
So much data is being collected these days. Machine learning is being used to analyze them and draw actionable insights. It is being used to not just understand static patterns but to predict things that have not yet happened. Predicting what items someone is likely to buy or which customers are likely to churn, detecting financial fraud, finding anomalous patterns, finding relevant documents or images on the web. These applications are changing the way people do business, find information, entertain and socialize, and so much of it is powered by machine learning. So it has great practical use.
For me, an extra exciting part of it is to witness applied mathematics at work. Data presents different aspects of reality, and my job as a machine learning practitioner is to piece them together, using math. It is often treacherous and difficult. The saying goes “Lies, lies, and statistics.” It’s completely true; I often arrive at false conclusions and have to start over again. But it is so cool when I’m able to peel away the noise and get a glimpse of the underlying “truth.” When I’m getting nowhere, it’s frustrating. But when I get somewhere, it’s absolutely beautiful and gratifying.

6. How do you go about framing a data problem – in particular, how do you avoid spending too long, how do you manage expectations etc. How do you know what is good enough? 
Oh! I know the answer to this question: before embarking on a project, always think about “what will success look like? How would I be able to measure it?” This is a great lesson that I learned from mentors at Microsoft Research. It’s saved me from many a dead end. It’s easy to get excited about a new endeavor and all the cool things you’ll get to try out along the way. But if you don’t set a metric and a goal beforehand, you’ll never know when to stop, and eventually the project will peter out. If your goal IS to learn a new tool or try out a new method, then it’s fine to just explore. But with more serious work, it’s crucial to think about evaluation metrics up front.

7. You spent sometime at other firms before Dato. How did you manage cultural challenges, dealing with stakeholders and executives? What advice do you have for new starters about this?
I think this is a continuous learning experience. Every organization is different, and it’s incredible how much of a leader’s personality gets imprinted upon the whole organization.  I’m fascinated by the art and science behind creating successful organizations. Having been through a couple of very different companies makes me more aware of the differences between them. It’s very much like traveling to a different country: you realize that many of the things you took for granted do not actually need to be so. It makes me appreciate diversity. I also learn more about myself, about what works and what doesn’t work for me.
How to manage cultural challenges? I think the answer to that is not so different between work and life. No matter what the circumstance, we always have the freedom and the responsibility to choose who we want to be. How I work is a reflection of who I am. Being in a new environment can be challenging, but it can also be good. Challenge gets us out of our old patterns and demands that we grow into a new way of being. For me, it’s helpful to keep coming back to the knowledge of who I am, and who I want to be. When faced with a conflict, it’s important to both speak up and to listen. Speaking up (respectfully) affirms what is true for us. Listening is all about trying to see the other person’s perspective. It sounds easy but can be very difficult, especially in high stress situations where both sides hold to their own perspective. But as long as there’s communication, and with enough patience and skill, it’s possible to understand the other side. Once that happens, things are much easier to resolve.

8. How do you explain to C-level execs the importance of Data Science? How do you deal with the ‘educated selling’ parts of the job?
I point to all the successful examples of data science today. With successful companies like Amazon, Google, Netflix, Uber, AirBnB, etc. leading the way, it’s not difficult to convince people that data science is useful. A lot of people are curious and need to learn more before they make the jump. Others may have already bought into it but just don’t have the resources to invest in it yet. The market is not short no demand. It is short on supply: data scientists, good tools, and knowledge. It’s a great time to be part of this ecosystem!

Interview with a Data Scientist: Maria Rosario Mestre

Standard

I recently caught with with Maria Rosario Mestre – she shared her personal views on Data Science – like all these interviewee subjects – these do not reflect her employers views.

Linkedin profile picture

Biography – Maria: 

I completed a PhD in signal processing at Cambridge developing models of user behaviour using brain data. After the PhD I joined Skimlinks as a data scientist, where I model online user behaviour and work on much larger datasets. My main role is implementing large-scale machine learning models processing terabytes of data.


What project have you worked on do you wish you could go back to, and do better?

I think that pretty much applies to any project you do as a data scientist. When you’re developing algorithms that become a service used by someone either internally or externally, I think it is best to use an iterative approach where you wait for some feedback from the client before doing any further improvements. I am a true believer of “lean data science”.


What advice do you have to younger analytics professionals and in particular PhD students in the Sciences?

I guess it depends what the advice is for . If is it for PhD students thinking about a career as a data scientist in industry, then I would strongly recommend them to get some experience working on real-world data at some point during the PhD. It is quite common in academia to work mainly on synthetic data. In addition to that, I would say it is important to keep a curious and open mind about the research carried out by other people, since it is very easy to only stay focused on your specific research project. For analytics professionals, I would say that learning how to code is quite useful, especially in a scripting language like Python. Knowing some classical statistics is also very helpful, if you want to learn how to apply a scientific approach to any type of data analysis.


What do you wish you knew earlier about being a data scientist?

There is not much I can think about, but maybe I wish I had spent more time using version control platforms, such as github. During my PhD I had a very rudimentary version control method: copying my whole project into a different folder with today’s date. It was definitely not the best way of managing my project. In my current role we work on a shared codebase and we need to keep track of changes, so I had to start using github. I wish I had taken more time to learn how to use it properly before diving into it, as it would have saved me a lot of time.


How do you respond when you hear the phrase ‘big data’?

I say that’s boring, now it’s all about “massive data”! Now seriously, I have experienced big data at Skimlinks, where we run daily jobs on terabytes of data using Spark. I think “big data” is a real thing, but people sometimes believe they have it when they don’t, or if they have it, then they think they need to do something about it, but don’t know what. I don’t think that you should approach “big data” as a solution in search of a problem. You should always think of the problem first that you’re trying to solve, see if your data scale qualifies as “big data”, and then finally start using big data tools once you have defined all these parameters. It is a waste of time and resources to start using these tools just because they are fashionable and you’re scared of missing out.


What is the most exciting thing about your field?

I find solving real problems exciting, and if these problems are hard, then it’s double as exciting. As a data scientist, you have to solve hard problems all the time, mainly because real data is never like in the textbooks! It’s always biased, with missing columns or wrong values. Then, I also find it exciting to solve problems with large-scale data. It is very easy to use out-of-the-box Python libraries to run a machine learning algorithm, but what happens when you have to adapt that algorithm to run on 500 gigabytes? That’s when you need to start thinking creatively using the tools you already know to solve a new problem. You might even be the first person to solve such a problem!

In more general terms, I think that machine learning will have a huge impact on our daily lives. We have already started seeing the effects now that we are always connected and use increasingly intelligent apps, but I think this is only the beginning.


How do you go about framing a data problem – in particular, how do you avoid spending too long, how do you manage expectations etc. How do you know what is good enough? 

This is a great question, and one that I keep asking myself. As I said earlier, I believe in lean data science. What this means is that I believe you need to start with a very clear objective you are trying to solve and use an iterative approach over it, always gathering feedback from the end user. If possible, the end goal should be stated in clear objective metrics, like increasing the accuracy of a classifier by 10%, or make better recommendations in 20% of the cases. You know it’s good enough when the end user is happy. I also believe that sometimes when you look at a problem from a lot of different angles and don’t seem to make a lot of progress, it is good to document all the attempts, leave it on the side, and get back to it later with a fresh pair of eyes.


How do you explain to C-level execs the importance of Data Science? How do you deal with the ‘educated selling’ parts of the job?

As a data scientist, your role is not only to develop algorithms, but also to be an evangelist in your own company on the use of data science, and generally the scientific method. If you want to convince business people that data science is important, then the best you can do is talk business. You need to think of data science projects in terms of the value they can add to your business, either because they can increase conversion rates, or keep some customers happy, or make someone’s job in the company much easier… You can start by running small experiments and gather some results to show to the executives in your company. However, data science is not the solution to any problem, and sometimes a simple rule-based model could do the job just as well. It is important not to oversell what you can do, and be realistic about what you can offer.


What is the most exciting thing you’ve been working on lately and tell us a bit about?

Skimlinks is about to launch a new product in the coming weeks, and the data science team has been heavily involved in its making. I cannot say much about it unfortunately, but these are exciting times for the company. From a technical point of view, the last thing that I have done which was exciting was classifying 1.2 billion data points using Spark. I broke a personal record in terms of the size of the data involved.


What is the biggest challenge of building a data science team?

I would have to ask my manager, since I have never built a team myself. I have been involved in the hiring process though, and I think it is sometimes difficult to find the right combination of skills across the team. You want some people who have experience working with data, others than may be stronger in engineering. It is also important to manage people’s expectations about the role, since data scientists spend a lot of time doing data processing and setting up data pipelines before they can apply machine learning algorithms. It’s all part of the job!

Interview with a Data Scientist: Erin Shellman

Standard
I recently caught up with Erin for an interview. Her interview is full of nice pieces of hard-earned advice and her final answer on Data Governance is gold!
Erin does some great blog posts at her blog, which I recommend. Erin is a programmer + statistician working as a research scientist at Amazon Web Services. Before that she was a Data Scientist in the Nordstrom Data Lab, where she primarily built product recommendations for Nordstrom.com. She mostly codes in Scala, Python and R, but dabbles in Javascript to put data on the internet. Erin loves to teach and speak, and does both often through talks, as co-organizer of PyLadies-Seattle, and as an instructor at the University of Washington’s Professional and Continuing Education program.
 
1. What project have you worked on do you wish you could go back to, and do better?
Often the goal of data science projects is to automate processes with data–I worked on a lot of projects at Nordstrom with that goal. I think we were pretty naive in those pursuits, often approaching the problems with low empathy and EQ (Emotional Quotient). We built tools, expecting that the teams we were trying to automate would immediately see the value and jump to use them, but we didn’t spend a lot of time listening and trying to understand why some might be hesitant to adopt our tools. Eventually, I started training people and specifically asking them to send bug reports or feature requests. The trainings opened up dialog about our plans and made the other teams more invested, because they could see when their bugs were fixed and their feature implemented. I learned that doing the data work is only half (or less) of the challenge, the other is advocating for your work in such a way that others are similarly compelled.
2. What advice do you have to younger analytics professionals and in particular PhD students in the Sciences?
If you’re in school right now, use this time to master a programming language (you have more time than you ever will again despite what you may believe). For data science, I’d recommend Python, R or Scala (and if you had to choose one, Python). You absolutely need to be able to produce high-quality code before you walk in the door because chances are you’ll be asked to code early in the interview process.
I also think you shouldn’t spend too much time “training” and learning in your free time, it’s nearly impossible to retain knowledge that way. Instead, spend all your time shoring up the essentials and work on getting a job immediately. You’ll learn so much more on the job than you could ever hope to on your own, plus you’ll be paid. Don’t wait for postings for junior data scientists (I don’t know that I’ve ever even seen one), contact employers you’re interested in working with directly and ask them to make that role for you. You should look for places where you know there’s a solid data team already so you have plenty of people to learn from. Academics tend to have a sort of learned helplessness because they’re so often not in control of their work or careers. This is not the case in industry, if you want something, don’t wait for it to come to you (it won’t). Be an active participant in your future.
3. What do you wish you knew earlier about being a data scientist?
I wish I had spent more time in grad school learning computer science. Often DS (Data Science) jobs end up being almost the same as CS (Computer Science) jobs, and in my case I had to pick up a lot of CS skills on the job.
4. How do you respond when you hear the phrase ‘big data’?
Usually by rolling my eyes so far into the back of my head that they get stuck. I think the return on investment of Any Data is still higher than that of Big Data. Most shops who’re convinced that they need big data technology don’t make use of the data they have already, and adding more data to the pile won’t help the cause.
5. What is the most exciting thing about your field?
The most exciting thing is that I get to learn for a living. Every time I switch jobs or work on something new I have to learn a ton, different technologies and languages, different domains, and different businesses. I especially love that data science is often so close to the business. I love learning about what makes a business successful and providing knowledge to help businesses make better decisions.
6. How do you go about framing a data problem – in particular, how do you avoid spending too long, how do you manage expectations etc. How do you know what is good enough? 
When I’m approaching a new problem I focus really hard on the inputs and outputs, particularly the output. What exactly are you trying to produce, or trying to answer? This is often a question I pose to business stakeholders to encourage them to think critically about and what they really want to know, how it will be applied, and how to formally articulate it. Basically what I encourage them to do is state a formal hypothesis and the observations required to test that hypothesis. Once we’ve all agreed on the output, what are the inputs? I try to make this as specific as possible, so no “customer data”-level descriptions. Tell me exactly what the inputs are, e.g. annual customer spend, age, and zip code. The more you can reason through the solution in terms of inputs and outputs before you set out to solve the problem the less likely it will be that you’re halfway to answering a question that was ill-posed (I promise, this is 90% of requests), or that you don’t have data to support (this is probably another 5% of requests). It’s also a good way to prevent “stakeholder punting” which is a phrase I made up just now to describe when stakeholders make half-baked requests and then leave them for you to sort out. Data science and research is highly collaborative, and the data scientist shouldn’t be the only one invested in the work.
Once the inputs and outputs are defined, I like to draw flowcharts of the path to completion, and it’s usually easier to start from the bottom. Here’s an example I created for the students in my data mining course. They were working on prediction of a continuous outcome with various regression methods. First we decided on a criteria for model selection, which in this case was the model with the lowest root mean squared error. You can see that the input is a data file, and the output is whichever model had the best predictive accuracy as measured by the lowest RMSE (Root Mean Square Error). For me, diagramming your work like this makes your goal completely concrete.
 Inline image 1
The other really great thing about framing problems this way is that it makes it very easy to estimate effort and communicate to others what is required to complete the projects. For whatever reason, people often assume that while software engineers need 2 weeks to add a minor feature, data scientists need about 6 hours to do complete analyses and make beautiful visualizations. Communicating the amount of work required to complete projects to the requesters is crucial in data science, because most people just don’t know. It’s not something software engineers typically have to do, but providing guidance on the components of a data science project to your stakeholders will reduce your stress in the long-run.
7. What does data governance or data quality mean to you as a data scientist?
Data governance is the collection of processes and protocols to which an organization conforms to insure data accuracy and integrity. Most of the time I’m a data consumer, so I depend on a mature data infrastructure team to create the pipelines I use to collect and analyze data. When I was working on recommendations at Nordstrom, I was a consumer and provider. I provided data in the sense that the output of my recommendation algorithms was data consumed by the web team. Data governance in that context meant writing lots of unit tests to make sure the results of my computations produced correctly formatted entries. It also meant applying business rules, for example, removing entries for products out of stock, or applying brand restrictions.

Interviews with Data Scientists: David J. Hand

Standard

I recently reached out as part of my Data Science interview series to David J. Hand.

David has an impressive biography and has contributed a lot to fraud detection and data mining. His answers are insightful and from a statistical point of view. I feel that these academics have a lot to teach us practicing data scientists.

  1. What project have you worked on do you wish you could go back to, and do better?

I think I always have this feeling about most of the things I have worked on – that, had I been able to spend more time on it, I could have done better. Unfortunately, there are so many things crying out for one’s attention that one has to do the best one can in the time available. Quality of projects probably also has a diminishing returns aspect – spend another day/week/year on a project and you reduce the gap between its current quality and perfection by a half. Which means you never achieve perfection.

  1. What advice do you have to younger analytics professionals and in particular PhD students in the Sciences?

I generally advise PhD students to find a project which interests them, which is solvable or on which significant headway can be made in the time they have available, and which other people (but not too many) care about. That last point means that others will be interested in the results you get, while the qualification means that there are not also thousands of others working on the problem (because that would mean you would probably be pipped to the post).

  1. What do you wish you knew earlier about being a statistician? What do you think industrial data scientists have to learn from this?

I think it is important that people recognise that statistics is not a branch of mathematics. Certainly statistics is a mathematical discipline, but so are engineering, physics, and surveying, and we don’t regard them as parts of mathematics. To be a competent professional statistician one needs to understand the mathematics underlying the tools, but one also needs to understand something about the area in which one is applying those tools. And then there are other aspects: it may be necessary, for example, to use a suboptimal method if this means that others can understand and buy in to what you have done. Industrial data scientists need to recognise the fundamental aim of a data scientist is to solve a problem, and to do this one should adopt the best approach for the job, be it a significance test, a likelihood function, or a Bayesian analysis. Data scientists must be pragmatic, not dogmatic. But I’m sure that most practicing data scientists do recognise this.

  1. How do you respond when you hear the phrase ‘big data’?

Probably a resigned sigh. ‘Big data’ is proclaimed as the answer to humanity’s problems. However, while it’s true that large data sets, a consequence of modern data capture technologies, do hold great promise for interesting and valuable advances, we should not fail to recognise that they also come with considerable technical challenges. The easiest of these lie in the data manipulation aspects of data science (the searching, sorting, and matching of large sets) while the toughest lie in the essentially statistical inferential aspects. The notion that one nowadays has ‘all’ of the data for any particular context is seldom true or relevant. And big data come with the data quality challenges of small data along with new challenges of its own.

  1. What is the most exciting thing about your field?

Where to begin! The eminent statistician John Tukey once said ‘the great thing about statistics is that you get to play in everyone’s back yard’, meaning that statisticians can work in medicine, physics, government, economics, finance, education, and so on. The point is that data are evidence, and to extract meaning, information, and knowledge from data you need statistics. The world truly is the statistician’s oyster.

  1. Do you feel universities will have to adapt to ‘data science’? What do you think will have to be done in say mathematical education to keep up with these trends?

Yes, and you can see that this is happening, with many universities establishing data science courses. Data science is mostly statistics, but with a leavening of relevant parts of computer science – some knowledge of databases, search algorithms, matching methods, parallel processing, and so on.

______________________________

Professor David J. Hand

Imperial College, London

Bio: David Hand is Senior Research Investigator and Emeritus Professor of Mathematics at Imperial College, London, and Chief Scientific Advisor to Winton Capital Management. He is a Fellow of the British Academy, and a recipient of the Guy Medal of the Royal Statistical Society. He has served (twice) as President of the Royal Statistical Society, and is on the Board of the UK Statistics Authority. He has published 300 scientific papers and 26 books. He has broad research interests in areas including classification, data mining, anomaly detection, and the foundations of statistics. His applications interests include psychology, physics, and the retail credit industry – he and his research group won the 2012 Credit Collections and Risk Award for Contributions to the Credit Industry. He was made OBE for services to research and innovation in 2013.

Interview with a Data Scientist: Ian Ozsvald

Standard

Ian Ozsvald is a Data Scientist based in London. He’s a friend and an inspiration to all us data geeks. He’s a co-organizer of PyData in London and speaks a lot on the data science circuit. He’s also very tall 🙂

I include a bio at the bottom.

1. What project have you worked on do you wish you could go back to, and do better?
My most frustrating project was (thankfully) many years ago. A client gave me a classification task for a large number of ecommerce products involving NLP. We defined an early task to derisk the project and the client provided representative data, according to the specification that I’d laid out. I built a set of classifiers that performed as well as a human and we felt that the project was derisked sufficiently to push on. Upon receiving the next data set I threw up my arms in horror – as a human I couldn’t solve the task on this new, very messy data – I couldn’t imagine how the machine would solve it. The client explained that they wanted the first task to succeed so they gave me the best data they could find and since we’d solved that problem, now I could work on the harder stuff. I tried my best to explain the requirements of the derisking project but fear I didn’t give a deep enough explanation to why I needed fully-representative dirty data rather than cherry-picked good data. After this I got *really* tough when explaining the needs for a derisking phase.
2. What advice do you have to younger analytics professionals and in particular PhD students in the Sciences?

You probably want an equal understanding of statistics, linear algebra and engineering, with multiple platforms and languages plus visualisation skills. You probably want 5+ years experience in each industrial domain you’ll work in. None of this however is realistic. Instead focus on some areas that interest you and that pay well-enough and deepen your skills so that you’re valuable. Next go to open source conferences and speak, talk at meetups and generally try to share your knowledge – this is a great way of firming up all the dodgy corners of your knowledge. By speaking at open source events you’ll be contributing back to the ecosystem that’s provided you with lots of high quality free tools. For me I speak, teach and keynote at conferences like PyDatas, PyCons, EuroSciPys and EuroPythons around the world and co-run London’s most active data community at PyDataLondon. Also get involved in supporting the projects you use – by answering questions and submitting new code you’ll massively improve the quality of your knowledge.

3. What do you wish you knew earlier about being a data scientist?
 I wish I knew how much I’d miss not paying attention to classes in statistics and linear algebra! I also wish I’d appreciated how much easier conversations with clients were if you have lots of diagrams from past projects and projects related to their data – people tend to think visually, they don’t work well from lists of numbers.
4. How do you respond when you hear the phrase ‘big data’?

Most clients don’t have a Big Data problem and even if they’re storing huge volumes of logs, once you subselect the relevant data you can generally store it on a single machine and probably you can represent it in RAM. For many small and medium sized companies this is definitely the case (and it is definitely-not-the-case for a company like Facebook!). With a bit of thought about the underlying data and its representation you can do things like use sparse arrays in place of dense arrays, use probabilistic counting and hashes in place of reversible data structures and strip out much of the unnecessary data. Cluster-sized data problems can be made to fit into the RAM of a laptop and if the original data already fits on just 1 hard-drive then it almost certainly only needs a single machine for analysis. I co-wrote O’Reilly’s High Performance Python and one of the goals of the book was to show that many number-crunching problems work well using just 1 machine and Python, without the complexity and support-cost of a cluster.

5. What is the most exciting thing about your field?

We’re stuck in a world of messy, human-created data. Cleaning it and joining it is currently a human-level activity, I strongly suspect that we can make this task machine-powered using some supervised approaches so less human time is spent crafting regular expressions and data transformations. Once we start to automate data cleaning and joining I suspect we’ll see a new explosion in the breadth of data science projects people can tackle.

6. How do you go about framing a data problem – in particular, how do you avoid spending too long, how do you manage expectations etc. How do you know what is good enough? 

To my mind the trick is figuring out a) how good the client’s data is and b) how valuable it could be to their business if put to work. You can justify any project if the value is high enough but first you have to derisk it and you want to do that as quickly and cheaply as possible. With 10 years of gut-feel experience I have some idea about how to do this but it feels more like art than science for the time being. Always design milestones that let you deliver lumps of value, this helps everyone stay confident when you hit the inevitable problems.

7. You spent sometime as a Consultant in Data Analytics. How did you manage cultural challenges, dealing with stakeholders and executives? What advice do you have for new starters about this?
Justify the business value behind your work and make lots of diagrams (stick them on the wall!) so that others can appreciate what you’re doing. Make bits of it easy to understand and explain why it is valuable and people will buy into it. Don’t hide behind your models, instead speak to domain experts and learn about their expertise and use your models to backup and automate their judgement, you’ll want them on your side.
8. You have a cool startup can you comment on how important it is as a CEO to make a company such as that data-driven or data-informed?

My consultancy (ModelInsight.io) helps companies to exploit their data so we’re entirely data-driven! If a company has figured out that it has a lot of data and it could steal a march on its competitors by exploiting this data, that’s where we step in. A part of the reason I speak internationally is to help companies think about the value in their data based on the projects we’ve worked on previously.

Bio: 

My name is Ian Ozsvald. I’m an Entrepreneurial Geek, 30-late-ish, living in London (after 10 years in Brighton and a year in Latin America).

I take on work in my Artificial Intelligence consultancy (Mor Consulting Ltd.) and I also authorThe Artificial Intelligence Cookbook – learn how to add clever algorithms to your software to make it smarter! One of my mobile products is SocialTies (built with RadicalRobot).

I co-founded ShowMeDo.com in 2005, it is all about tutorial screencasts that teach you programming, see About ShowMeDo for more info.  This was my second company and I’m rather proud to say that it is financially self-sufficient, growing and is full of very useful user-generated (and us-generated) content.  100,000 users and 1TB of data served per month say that we built some very useful indeed. In 5 years ShowMeDo has educated over 3 million people about open source tools.

I’m also co-founder of the £5 Apps Meetup, OpenCoffee Sussex and the BrightonDigital mail list (RIP).

Previously I’ve worked as Senior Programmer at Algorithmix (now Corpora) and the MASA Group, and these jobs came via my MSc in Artificial Intelligence at Sussex University.  See myLinkedIn profile.

Interviews with Data Scientists: Vanessa Sabino

Standard
Time for another Interview with a Data Scientist.
I caught up with Vanessa Sabino who is a lead data scientist in another one of Shopify’s teams. 
1. What project have you worked on do you wish you could go back to, and do better?
1. Working as practitioner in a company, as opposed to consulting, means I always have the option of going back and improving past projects, as long as the time spent on this task can be justified. There are always new ideas to try and new libraries being published, so as a team lead I try to balance the time spent on higher priority tasks, which for my team currently is ETL work to improve our data warehouse, with exploratory analysis of our data sets and creating and improving models that add value to our business users.

2. What advice do you have to younger analytics professionals and in particular PhD students in the Sciences?
2. My advice is to not underestimate the importance of communication skills, which goes from listening, in order to understand exactly what the data means and the context in which it is used, to presenting your results in a way that demonstrates impact and resonates with your audience.
3. What do you wish you knew earlier about being a data scientist?
3. I wish I knew 20 years ago how to be a data scientist! When I was finishing high school and I had to decide what to do in university, I had some interest in Computer Science, but I had no idea what a career in that area would be like. The World Wide Web was just starting, and living in Brazil, I had the impression that all software developing companies were north of the Equator. So I decided to study Business, imagining I’d be able to spend my days using spreadsheets to optimize things. During the course I learned about data warehouses, business intelligence, statistics, data mining and decision science, but when it was over it was not clear how to get a job where I could apply this knowledge. I went to work on a IT consulting company, where I had the opportunity to improve my software developing skills, but I missed working with numbers, so after two years I left to start a new undergrad in Applied Mathematics, followed by a Masters in Computer Science. Then I continued working as a software developer, now in web companies, and that’s when I started learning about the vast amount of online behavior data they were collecting and the techniques being used to leverage its potential. “Data scientist” is a new name for something that covers many different traditional roles, and a better understanding of the related terms would have allowed me to make this career move sooner.

4. How do you respond when you hear the phrase ‘big data’?
4. I prefer to work closer to data analysis than to data engineering, so in an ideal world I’d have a small data set with a level of detail just right to summarize everything that I can extract from that data. Whatever size the data is, if someone is calling it big data it probably means that the tool they are using to manipulate it is no longer meeting certain expectations, and they are struggling with the technology in order to get their job done. I find it a little frustrating when you write correct code that should be able to transform a certain input to the desired output, but things don’t work as expected due to a lack of computing resources, which means you have to do extra work to get what you want. And the new solution only lasts until your data outgrows it again. But that’s just the way it is, and being in the boundary of what you can handle means you’ll be learning and growing in order to overcome the next challenges.

5. What is the most exciting thing about your field?
5. I’m excited about the opportunities to collaborate in a wide range of projects. Nowadays everyone wants to improve things with data informed decisions, so you get to apply your skills to many areas and you learn a lot in the process.

6. How do you go about framing a data problem – in particular, how do you avoid spending too long, how do you manage expectations etc. How do you know what is good enough? 
6. I always like to start with simple proof of concepts and iterate from there, using feedback from stakeholders to identify where are the biggest gains so that I can pivot the project in the right direction. But the most important thing in this process is to constantly ask “why”, in particular when dealing with requests. This helps you validate the understanding of the problem and enables you to offer better alternatives that the business user might not be aware of when they make a request.
And for the bio:
Vanessa Sabino started her career as a system analyst in 2000, and in 2010 she jumped at the opportunity to start working with Digital Analytics, which brought together her educational background in Business, Applied Mathematics, and Computer Science. She gained experience from Internet companies in Brazil before moving to Canada, where she is now a data analysis lead for Shopify, transforming data into Marketing insights.