Who is Peadar?


Analytics. Statistics. Coding. Strategy. Tech. Lean.

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I’m Peadar Coyle, a data scientist and author specialising in applying robust statistical or machine learning models to big/medium/small data to extract business value such as new revenue streams or business process optimisation. I am currently living in Luxembourg, but willing to relocate throughout Europe for the right next opportunity.

I’ve been in the analytics space for a few years now, and I’m interested in developing in a senior fashion. I’m passionate about solving what I see is the ‘last-mile’ problem of Data Science, which is getting the insights into action. 

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Applying Machine Learning in Industry

I’ve recently been consulting with some startups and public companies, this exposed me to conversion analysis, marketing analysis and product analysis.

Before that I was leading Energy Analysis projects for Vodafone Procurement Company, where I helped them on their ‘Big Data’ journey; by delivering market research, robust quantitative analysis, analytical forecasting and designing and delivering novel KPI’s for a major streamlining initiative of a bill-to-pay process. I also delivered a novel machine learning model applied to predicting demand of energy consumption leveraging Internet-of-Things.

Previously I’ve applied my quantitative skills to optimising inventory placement at Amazon.com, built a Wake Vortex Turbulence simulation engine at A-Syst SA a small IT consultancy – deployed via a micro service architecture, analysed web data at Import.io in London and worked on strategy and marketing analytics at Letsface in Shanghai.


My academic experience includes research on Semi-classical Optics and Quantum Computing at the University of Bristol and doing time series forecasting at University of Luxembourg. My research interests in Mathematics involved the application of statistical learning techniques (Machine Learning) to time series prediction problems in the context of economic forecasting. During graduate school I took many courses in Mathematics, Statistics and Machine Learning.  My conference papers since then have mostly focused on Bayesian Statistics and it’s applications to Sports Analytics. I’m continually fascinated by the estimation and quantification of prediction risk and feel this extend into my work as a Quant or Data Scientist.

Conferences, writing and Open Source Contributions

My writing has been featured on Dataconomy, I’ve spoken at multiple meetups, chaired panel discussions, conferences such as and PyCon Italia. I also delivered well received talks on Bayesian Statistics and Deploying Data Science models in production at PyCon Ireland. I was very glad to see such cool Data Science work happening in Ireland!

I co-organize the Data Science Meetup in Luxembourg, which has grown to over 500 members. Some of my talks have over 1000 viewers on Youtube, and my expert-level PyData London tutorial on Markov Chain Monte Carlo (MCMC) and Hamiltonian MCMC models  was attended by over 100 people.

I’ve made some open source contributions to popular PyData libraries such as PyMC3 and Pandas.

Technical Skills

My technical skills are varied but I predominately use Python, R, SQL but I’ve been recently experimenting with leveraging the power of Spark and Scala. As a typically type A (analytics and insights) data scientist I’ve been focusing recently on developing my ability to write deployable production code. This has involved me learning more and more about software engineering, high performance computing and JVM languages like Java and Scala. My Amazon and consulting experience exposed me to NoSQL technologies such as Hive, Spark, DynamoDB and Cassandra. I’m familiar with leveraging the power of AWS, clusters and GPU’s but don’t call myself an expert (yet). 

Communication skills and expertise

As I mentioned already my Masters is from the University of Luxembourg in Mathematics, which I did part-time paying my way by tutoring A levels students. After my Bachelors from Bristol I spent some time both interning in startups and tutoring Physics, computer science and Mathematics in a school. I feel this experience has helped me develop good communication skills and is one reason I enjoy giving trainings and talks to both technical and non-technical audiences.

My series of interviews with other data analytics professionals is being turned into a book, which is available from my website.

Linkedin https://lu.linkedin.com/in/peadarcoyle

Blog and personal website – https://peadarcoyle.wordpress.com/

Github https://github.com/springcoil

Dataconomy http://dataconomy.com/author/pcoyle/

Interviews with Data Scientists https://leanpub.com/interviewswithdatascientists/

Skills: Statistics, Data Analysis, SQL, Predictive Analytics, Python, Numpy, Scikitlearn, EC2, High Performance Computing

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