Interview with a Data Scientist – Ian Wong of OpenDoor

I interviewed the interesting and fascinating Ian Wong – he’s the technical co-founder of OpenDoor, which I personally think is amazing as a concept!
(Ian Wong – Source: Linkedin)
1. What project have you worked on do you wish you could go back to, and do better?
Pretty much any project I’ve worked on in the past smiling face with open mouth Two projects stick out though.
a. Uniform API for Data Fetching
When we fit a model, call it y = f(X),  (X, y) are often taken for granted to be well-formed. How do you design a service that generates consistent (X, y)? Turns out this is not straightforward, and is specific to the domain and the data-capture systems.
The ideal solution would satisfy both batch & real-time needs; makes it easy to ship new features to production; and enables rapid prototyping. While I scrapped together the initial version at Opendoor, our amazing team of data engineers have really taken it to the next level. We hope to report our findings soon.
b. Interactive Visualizations of ML Algorithms
A few years ago, I tried putting together a visualizer for random forests using d3. I had the tremendous fortune of working with Mike Bostock for a bit, and was inspired by his ability to make abstract concepts tangible through interactive visualizations. At the time, I was working with these big sets of random forests, and wanted a get a better feel of the model outputs. So I rendered hundreds of decisions trees on screen, where when you hovered over one node, all other nodes belonging to the same features across different trees would be highlighted. It was pretty neat! But the prototype suffered from performance issues plus my own technical incompetence.
More broadly, I’m really excited about better interactive tools for ML algorithms because they’ll help us deeply understand them as tools. During my undergrad studies in electrical engineering, we used to play with circuits a lot. In the labs, you could make a change to the input or to the circuitry, and see the corresponding change in output on the oscilloscope instantaneously. When we run a simple regression, wouldn’t it be great to get immediate feedback on the fitted line if we were to drag, add, delete a data point?
I’m hopeful we’ll see more innovation in the read-eval-print loop for data science.
2. What advice do you have to younger analytics professionals and in particular PhD students in the Sciences?
My view may be slightly biased as I’m a PhD drop-out winking face In grad school, I became increasingly frustrated at the divergence between what’s interesting and what’s impactful. But that’s a whole separate conversation.
For folks looking to enter industry, nothing replaces hands-on practice. I would strongly encourage students to look for internships, participate in Kaggle competitions / Google Summer of Code, seek open source projects to contribute to. If you’re in school, take a wide variety of classes, especially computer science and project-based courses.
The higher order bit here is that the industry faces a different, evolving set of challenges than academia. The focus is typically on solving a business problem.
Here are additional pointers depending on the reader’s interest.
Business Analytics & Decision Science
  • The grammar of graphics and tidydata lay a great foundation for reasoning about data. I personally learned more by working through Hadley’s ggplot2 book than from many of my stats classes at Stanford. 
  • Develop business acumen and communication skills. Sometimes academics prefer to stay in the rarified air of theory and mathematics. Success in the analytics profession requires the ability to (a) meet hard business challenges head on, (b) break them down into smaller, quantifiable sub-problems, (c) rapid analysis, (d) present findings in a way that the audience can engage with, (e) take feedback and iterate.
Machine Learning & Engineering
  • Code, code, code. As I mentioned in Doing Data Science: ML is founded in math, expressed in code, and assembled into software. Being able to build robust software systems is becoming more important, as tools and algorithms are increasingly available.
  • While a strong grasp of theory will help narrow design choices, nothing beats rapidly exploring hypotheses. This demands coding proficiency, which from experience is a differentiating trait of highly productive data scientists.
Also, don’t let your field tie you down! Beware of sunk cost fallacy. Though PhDs may have invested years studying a certain field, the techniques investigated through a graduate program may not be transferable to a new domain. The most important quality of the PhD is persistence in doing research. Remember, it’s re-search search and search again. That’s what defines a great problem solver.
3. What do you wish you knew earlier about being a data scientist?
There are so many! Here’s a few.
How to build great predictive services
While we spend a lot of energy in grad school studying techniques, advanced techniques often yield only incremental lift over a simple solution (and in many cases comes with complexity that becomes a heavy tax). I think the big focus on modeling techniques contributes to the phenomenon of solutions chasing problems, rather than solutions being designed from the needs of the problem. Here’s a rule of thumb that I’ve come to adopt: You know that algorithm that all the papers make fun of in their intro? Implement that and forget the rest of the paper.”
Perhaps influenced by schooling, we as data scientists often dream about having these flashes of brilliance that identifies a proof! QED! In practice, what delivers results is an error-focused, iterative process of continuous model improvement (see my talk here). It’s the unglamorous engineering & detective work of starting with the biggest outliers of the model, and reasoning from first principles to eliminate them. Model debugger describes the role better than data scientist. It’s about the toil.
Forming a perspective based on incomplete data
Intellectual honesty, scientific doubt and a healthy dose of paranoia are generally great things to have. But beware of analysis-paralysis and failure to put a stake in the ground. Decisions need to be made in a timely fashion. In many cases we’re operating with 80% information (if lucky!), and your teammates are counting on you for a recommendation.
Earlier in my career, I would be reluctant in forming and articulating a strong perspective. Partly due to skepticism inculcated through school, and partly because it didn’t seem like it was my job as a data scientist to do so (more on titles being a constraint later). Making an actual policy recommendation seems so messy relative to the clean code and beautiful plots staring at me on the monitor. But I’ve since learned that this is an abdication of responsibility. Our job is to help the company make data-driven decisions, which means thinking through the implications of an analysis, consulting stakeholders, and coming up with a point of view.
Communication as craft
The value of an analysis is measured by whether it influences decisions. Even the most brilliant analysis becomes ineffective if not delivered to the audience in an accessible manner. This Jeff Atwood blog post explains the concept well.
Other things!
There are many other things I wish I knew earlier. How do I pick up software engineering skills? What does a great data scientist look like? How do I progress to become better? How do I foster effective debate and engagement of my work? I’ll omit them for now since this is getting long…
4. How do you respond when you hear the phrase big data’? What about AI’?
On Big Data
There’s big data, and there’s Big Data. If you’re referring to the latter, I think it’s a bit passé at this point (with some exceptions).
Turns out that more data beats better algorithms most of the time. As an industry we have worked really hard to make count(x) group by y scale to terabytes of data. But as alluded to earlier, the tools and infrastructure are increasingly commoditized. We’re ready to move onto higher parts of the application stack vs. focusing on the base layer. (e.g., Opendoor! See also the vertical AI piece by Bradford Cross.)
Turns out machines are tireless and can count much more reliably than human beings. This has implications as we enter the age of abundant data. This can get philosophical quick! But there are both benefits and hazards we’ll need to navigate.
5. What is the most exciting thing about your field?
As technology and education improves and become more accessible, there’ll be an increased supply of data science and machine learning talent. These individuals will become the next generation builders and leaders. Algorithmic sophistication is going to seep into all parts of our daily lives. The products they create are going to be smarter, easier to use and more personal (Opendoor being an example smiling face with open mouth).
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 Alan Kay once said: A change in perspective is worth 80 IQ points. Framing a problem well is probably the most important part of the solution.
Within the context of building predictive services, defining the objective function with a clear metric that’s ideally back-testable is half the battle. It provides a foundation for the rest of the work, which entails applying the simplest approach, iterating until convergence to some threshold that’s set by business needs. The art comes in how to define the ML problem in a way that aligns with business outcome (ideally tracing all the way through to the top- or bottom-line).
In terms of when is good enough, that depends on the need. Running a company is kind of like trying to solve an NP-hard resource optimization problem. We have to be rigorous about ROI for each initiative that we spend energy on.
In terms of managing expectation, it’s hard. It folds into longer term project and team planning. What is the business problem we’re trying to solve? What does success mean? Where do we need to be today, a quarter from now, a year from now? Who are the stakeholders? How should we provide updates and receive feedback?
7. You’ve spoken about people not needing to be constrained by titles. Could you expand a bit on that? What sort of skills should someone with ML skills be learning in your opinion? What have you learned working at Opendoor?
On Being Boxed in by Titles and Venn Diagrams
Titles should enable, not constrain. We are all problem solvers first. A title acknowledges that an individual is skilled in a certain area. But one shouldn’t let that define their boundaries. When misused, titles could be a escape hatch to avoid doing the things that matter. For instance, a misinformed data scientist may think of productionizing their insight” as unnecessary implementation detail, while a misinformed software engineer may think of defining data quality SLA for predictive systems as esoteric. In practice, there’s a metric to move, a question to be answered. Titles endow neither immunity nor magical problem-solving powers. What matters is clarifying the job to be done.
In a PhD program, there’s a tendency to put blinders on and focus on one problem, specified by one professor in one department. In industry, solutions tend to be multi-disciplinary. A lot of what we do as data scientists is to take human intuition and generalize them, seeing which withstand the backtest or an experiment. And to do this well, we need to be open to new ideas and continuously develop new skills. As allude to earlier, some of the key ones are (a) business intelligence and (b) software engineering (including frontend!).
But I would be remiss not to mention the following
  • Scrappy + Pragmatic + Business Acumen > Technical Expertise
The best data scientists are relentlessly resourceful and impact- / solution-oriented. Mindset shifts from I need to gain skill x” to I am going to solve problem y”; from not my job” to run towards where the impact is.”
It’s been an incredible journey thus far at Opendoor. We are on a mission to empower everyone with the freedom to move by building a seamless, end-to-end customer experience that makes buying and selling a home stress-free and instant. The experience of growing our team, scaling up as a leader and servicing thousands of customers has been really rewarding. It’s the perfect blend of crazy-hard technical challenges and creating positive impact in people’s lives.
We’re only getting started! If any of this seems exciting, check out, or email me at
These are great questions and I had a fun time (partially) answering them!
Ian is working on modernizing the residential real-estate industry. He is the technical co-founder of Opendoor, where he leads their engineering and data science team. He previously built fraud detection systems at Square as their first data scientist, and products at Prismatic. Ian received his BS in EE (Electrical Engineering), MS in EE and MS in Statistics from Stanford before dropping out of the PhD.

Working in a major trend – Machine Learning


I saw recently this from the recent Amazon shareholder letter.

“These big trends are not that hard to spot…We’re in the middle of an obvious one right now: machine learning & artificial intelligence” — Jeff Bezos

One of the hard parts about working professionally on these technologies. Is I take them for granted. So I consider this a post to just reflect on the improvements in image processing, computer vision, translation, natural language processing, text understanding, forecasting, risk analysis.

I’ve worked on some of these technologies, and these challenges, and continue to work on extracting information from CVs and matching candidates to the best jobs for them at Elevate Direct. When you’re in the weeds you sometimes forget what you’re working on, and that you’re part of a major trend.

As Matt Turck says

Big Data provides the pipes, and AI provides the smarts.

AI in the Enterprise (the problem)


I was recently chatting to a friend who works as a Data Science consultant in the London Area – and a topic dear to my heart came up. How to successfully do ‘AI’ (or Data Science) in the enterprise. Now I work for an Enterprise SaaS company in the recruitment space, so I’ve got a certain amount of professional interest in doing this successfully.

My aim in this essay is to outline what the problem is, and provide some solutions.

Firstly it’s worth reflecting on the changes we’ve seen in Consumer apps – Spotify, Google, Amazon, etc – all of these apps have personalised experiences which are enhanced by machine learning techniques depending on the labelled data that consumers provide.

I’ll quote what Daniel Tuckelang (formerly of Linkedin) said about the challenges of doing this in the enterprise.

First, most enterprise data still lives in silos, whereas the intelligence comes from joining across data sets. Second, the enterprise suffers from weak signals — there’s little in the way of the labels or behavioral data that consumer application developers take for granted. Third, there’s an incentive problem: everyone promotes data reuse and knowledge sharing, but most organizations don’t reward it

I’ve personally seen this when working with enterprises, and being a consultant. The data is often very noisy, and while there are techniques to overcome that such as ‘distant supervision‘ it does make things harder than say building Ad-Tech models in the consumer space or customer churn models. Where the problem is more explicitly solvable by supervised techniques.

In my experience and the experience of others. Enterprises are much more likely to try buy in off-the-shelf solutions, but (to be sweepingly general) they still don’t have the expertise to understand/validate/train the models.There are often individuals in small teams here & there who’ve self-taught or done some formal education, but they’re not supported. (My friend Martin Goodson highlights this here)  There needs to be a cultural shift. At a startup you might have a CTO who’s willing to trust a bunch of relatively young data science chaps to try figure out an ML-based solution that does something useful for the company without breaking anything. And it’s also worth highlighting that there’s a difference in risk aversion between enterprises (with established practices  etc) and the more exploratory or R and D mindset of a startup.

The somewhat more experienced of us these days tend to have a reasonable idea of what can be done, what’s feasible, and furthermore how to convince the CEO that it’s doing something useful for his valuation.

Startups are far more willing to give things a go, there’s an existential threat. And not to forget that often Venture Capitalists and the assorted machinery expect Artificial Intelligence, and this is encouraged.

Increasingly I speculate that established companies now outsource their R and D to startups, hence the recent acquisitions like the one by GE Digital.

So I see roughly speaking two solutions to this problem. Two ways to de-risk data science projects in the enterprise.

1) Build it as an internal consultancy with two goals: identifying problems which can be solved with data solutions, and exposing other departments to new cultural thinking & approaches. I know of one large retailer who implemented this by doing 13 week agile projects, they’d do a consultation, then choose one team to build a solution for.

2) Start putting staff through training schemes similar to what is offered by General Assembly (there are others), but do it whole teams at a time, the culture of code review and programmatic analysis has to come back and be implemented at work. Similarly, give the team managers additional training in agile project management etc.

The first can have varied success – you need the right problems, and the right internal customers – and the second I’ve never seen implemented.

I’d love to hear some of the solutions you have seen. I’d be glad to chat about this.

Acknowledgements: I’d like to thank the following people for their conversations: John Sandall, Martin Goodson, Eddie Bell, Ian Ozsvald, Mick Delaney and I’m sorry about anyone else I’ve forgotten.


The Setup


The Setup has always been one of my favorite sites on the internet. I love seeing how other people – in vastly different careers – get their work done. Though I don’t craft Chinese soliders out of cardboard or anything nearly that fascinating, I thought it would be a fun exercise to put together my own version.

Who are you, and what do you do?

I’m Peadar Coyle, and I’m a data scientist based in Luxembourg, until recently I was at Vodafone as a Quantitative Analyst in their Energy team.  As you might expect, there are many people out there with that title and many do quite different work. My career has been varied so far, but I’m predominately a type A (for insights) data-scientist which means I spend half of my time coding and prototyping models to provide insights for business stakeholders. I’m working hard on improving my development skills so that I can deliver robust, working code in production. My intellectual background is in Physics and Mathematics.

I enjoy talking (as all Irish people do 🙂 ) so I regularly share my knowledge at conferences such as PyData.

What hardware do you use?

I use (and adore) my Leuchterm notebook (8″, with dots) for taking notes during phone calls, meetings, and any other times when typing on a laptop feels out of place or unnecessary. It’s a fantastic thought-collector for all manner of doodles, brainstorms, projects, and data visualisations. In that notebook (and everywhere else, really), I’ll write with whatever is around, but my preference is for ultra fine gel ballpoints.

Until recently I was using Moleskines, but I found them a tad expensive for their quality.


I carry a Samsung Galaxy J everywhere for all the uses in the world (+ multi-factor auth all the things). The battery is absolutely terrible, so I always keep a portable battery in my bag. That might actually be one of the most worthwhile 25 euros I’ve ever spent.

My home machine is a MacBook Pro (Retina, 15-inch, Mid 2014) with 16GB of RAM. This is a pretty hefty machine and quite difficult to carry around, but the retina screen is awesome.

For cloud computing (that counts as hardware right?) I use EC2 and S3 on AWS. For certain problems like Kaggle or complicated problems I’ll use whatever the most powerful machine I can get my hands on 🙂

And what software?

This is where I spent most of my time. I try out lots of tools to make my work (and life) easier. For me, “easier” is always a balance between “more tools that each do one thing well” and “fewer tools that each do all sorts of things.” It’s a constant work in progress.

I’m still using OS X 10.10 (Yosemite). When it comes to my work system, I’m rarely an early adopter because new OS updates always break environments.

I probably spend 50% of my time in OS X’s Terminal. Most of that time is spent in vim. I write most things there: code (mostly Python and bash), documents (Markdown, text, and TeX), etc. The solarized (dark) theme gives nice syntax highlighting contrast, and also keeps my eyes from getting tired (this will be a recurring theme). I keep meaning to try out iTerm but haven’t gotten around to it. I spend a lot of time working on remote Linux servers, so I tend to keep it simple (and similar) on my own machine. I’ll occasionally try to learn Emacs – and then give up and go back to Vim.

I’d guess the next 45% of my time is spent in Chrome. Among all the articles I’ve opened to read (but will inevitably drop into the Pocket black hole), you’ll pretty much always find some combination of tabs open that include: all Google Apps (mail, cal, drive, and a handful of docs), StackOverflow, the Python docsGitHub, Slack, trello, twitter and often a wikipedia page or two about whatever concept or technique I’m trying to grok at the moment. I’ve recently started using Safari books which is an expensive investment but it strikes me personally as a worthwhile one.

I recommend any data geek wanting to improve their productivity learn sed, awk and also use csvkit which I couldn’t live without.

I also use a bunch of Extensions because efficiency makes me incredibly happy: JSONView & XML Tree (prettify API responses), Markdown Reader (live rendering of local .md files – usually how I write and review these posts), Pocket (save-for-later), and Tab-Snap (store giant tab list as restorable text file)..

The last 5% of my time is spent switching between a host of other apps: Wunderlust (daily note-taking and long-term reference storage), Slack (team/org communication), Gimp (for my amateur image creation needs), Slides (for important presentations, GDocs for less important ones), and Toggl (time tracking; incredibly enlightening if you’ve never tried it). I also use Jupyter a lot but recently I’ve been moving to PyCharm  since I’m trying to write less ad-hoc stuff and more python modules. Since I’m trying to learn Scala at the moment I’ve been using IntelliJ which is an awesome IDE. I honestly don’t know how anyone codes in a JVM language without a good IDE.

There are a handful of other apps that are hugely valuable and always running in the background, too:  Dropbox (for both personal syncing – Camera Uploads! – and quick file sharing), Skypef.lux(adjusts your display’s color temperature – helps reduce eye strain when working at night).

What would be your dream setup?

Although I am close to it. Some small changes would include: a not-yet-possible 13″ MacBook Air with the specs of the burly 15″ Retina MBP, a pair of those magical Bose headphones I mentioned earlier, a couple of 27″ displays, and a beautiful, automatic sit-to-stand desk would be a nice start.

Sexism in Tech conferences


Writing about sexism in tech conferences is hard. Especially as a young white male. I can only speak anecdotally – but most women in the Tech industry I speak to, talk a bit about moments of subtle sexism or sometimes out-and-out harassment. As a member of the tech community I’m completely behind any promotion of minorities in the industry, and feel that more can be done. It is interesting that most men I speak to in the industry don’t notice any problem.

Two articles spring to mind:

This was written about STEM but I feel the same rules apply to the Tech community (especially since I personally straddle both communities).

It’s “not a big deal” when someone tells you he came to your talk because you’re attractive.
It’s “not a big deal” when a coworker comments on your appearance.
It’s “not a big deal” when someone makes a “joke” at work demeaning women.
It’s “not a big deal” when you are asked in a job interview if you have or are planning to have kids.
It’s “not a big deal” that you were warned about what professor to avoid basically as soon as you got to school.
It’s “not a big deal” that that same professor was untouchable by the administration because he was too famous.
It’s “not a big deal” when someone assumes you are your own secretary on the phone.
It’s “not a big deal” when someone calls you “Miss” and your male colleague “Doctor.”
It’s “not a big deal” when going to parties at a conference comes with warnings of which of your fellow scientists are dangerous.
It’s “not a big deal” when your boss, adviser, or senior colleague asks you out.
All of this stuff IS a big deal. One of the things I hear about the tech industry – partly because of the passive agression that Hackers sometimes adopt is that as a community we need to grow up and become more professional AND inclusive. I agree wholeheartedly with this and applaud the conferences that encourage more female participation and more female speakers. Diversity is a good thing and I think it makes us smarter :).
The other link I saw was about Defcon a famous security conference. I found the following paragraph to be very powerful.
When you say, “Women shouldn’t go to DEFCON if they don’t like it,” you are saying that women shouldn’t have all of the opportunities that come with attending DEFCON: jobs, education, networking, book contracts, speaking opportunities – or else should be willing to undergo sexual harassment and assault to get access to them. Is that really what you believe?
I am glad things are getting better but there are still a number of actions that we can all take. I think this is a subproblem of the larger problem that Pete Warden commented about. I consider his article to be self-recommending
Comments are welcome. The articles I linked to, contain some excellent resources on how to enforce or come up with policies in regards harassment – which is a legal issue. Lots of us like to avoid legal issues like this – but an advantage of policies and ‘processes’ is that they are transparent and fair. Some of us consider these things to be too formal – but as I get older I see that some of these ‘formalities’ that we have in corporations and other organizations are useful and save a lot of hassle.

David MacKay interview on Climate Change

Standard is a link to an interview with David MacKay one of the top civil servants on climate change, he is also the author of an excellent book.
Mathematical analysis offers a lot in understanding the challenges we face, and its great to see an eminent physicist being involved in such things.
What is very interesting is how important Nuclear Energy is in the calculations, its very difficult to reach power output without this happening.

Observations on the connectedness of our world.


Scientifically focused geeks like myself,have a tendency to speak highly of the web. We see Skype, MSN, and Facebook as great technical marvels. Yet as someone like Tim Ferris or Cal Newport observe there is a price to this connectedness.
Today for instance there was a family wedding in Ireland. I wasn’t able to attend due to examinations next week in my own studies. Yet through text messages and Skype conversations I feel like I’m half there.
Which means that concentration is difficult. Yet concentration is something I need to develop the rare and valuable skills of a Mathematician. Or whatever discipline I end up working in.
We should remember that we are fundamentally limited by the hardware of our brains. And limited by our humanity. We shouldn’t forget the effects of technology or modern day life on our cognitive load.

Mick Bremner wrote a post on this a few years ago.

Now, as time goes on and I realize that moving home every couple of years is actually taking a toll on my relationships with people that I care very much about I realize that, possibly, my writing can help the situation. I’m reluctantly realizing that I’m rarely ever going to be able to spend long afternoons chatting with my dearest friends over (good) coffee. But maybe if I keep this blog up to date then at least they might have some chance of keeping track of what’s going on with me.

My own Facebook and Twitter accounts have friends and family all around the world. I’ve friends who live in Hong Kong, Shanghai, New York, London, Adelaide and everywhere in between. And as Ben Casnocha pointed out, there is a ‘feel bad effect’ to Facebook of not-so-close-facebook-friends.
Constantly we see upbeat images, or happy occasions. Rarely the daily struggles of our existences. When we read CV’s or resumes of people in our respective fields we don’t hear about the struggles of their lives.
This is written to point out that everything I do in life seems to be an absolute struggle.