The data science group at Overstock tackles some of the hardest analytical problems facing our multifaceted, multibillion dollar business. The data science team isn't tied to any particular part of the company, but instead serves as a group of internal consultants for any area of the company that could benefit from improved understanding and utilization of our data. This can range from operational optimization to broad strategic advising.
The team works with large data sets to build algorithms and models. This requires an understanding of not just the mathematical and technical foundations of these models, but also how they will be used and extended by the business. The hard part isn't finding projects, the hard part is choosing among the many multimillion dollar opportunities that present themselves.
A data scientist is somebody who can look at a complicated technical problem, figure out the useful questions, do what is necessary to get the answers, and then implement solutions based upon those answers. How you think is more important than what you know. Or, at least, what you know today, because how you think will determine what you'll learn today and know tomorrow. A great data scientist is curious about learning and understanding as much about the business as he or she can, can direct that curiosity towards insights that drive major business improvements, and can communicate those insights effectively to anybody in the company who could benefit from them.
If this describes you, we'd love to talk about the opportunities available for you at Overstock.com.
Gather, clean, report, and interpret data rapidly and thoughtfully. Ingest terabytes of data, identify the critical signal in all that information, and then act on that signal to drive millions in growth or savings.
Perform ad hoc analyses using statistics, machine learning, and data mining techniques with whatever tool makes sense for the problem. Don't be limited to just one tool or approach. Let the problem, and not your past, drive the analysis.
In conjunction with above, perform self-directed ad-hoc analysis to identify drivers of business outcomes. Your manager's job shouldn't be to give you tasks. It should be to give you direction, and then provide whatever support you need to get there.
Report findings, recommendations, and conclusions resulting from analysis. The more people understand an insight, the more useful it is. Your insights can make a huge difference at Overstock, but only if they're understood.
Construct informative and insightful visualizations. A good visualization can be the difference between confusion and comprehension.
ETL and analysis of large scale data using Hadoop related tools. The world's data is getting big, and we need big tools and big brains to drive big change.
Evaluation of third-party analytics tools. There are some good tools out there, and we want to find the best.
Build tools that enable other teams to perform predictive analytics. Building tools not only makes others more productive, it frees up your time as well.
Use and support database applications. Understanding our data, and how it's used, is, not surprisingly, critical to data science.
Teach, counsel, and supervise the other analysts to help them realize their potential. If you're passionate about what you do, you should love sharing it with others.
Prepare lectures/presentations on tools and techniques that other analysts and data scientists should know. A great teacher can make others great as well.
Continuously research new data mining, machine learning, and analysis techniques. More important than what you know is what you can learn.
Continuously research new methods for analyzing data. Exciting new ideas are coming out every day, and we want to stay on top of them.
Perform other duties as required and assigned by manager and upper management.
Follow legal policies as directed.
Background in data-driven research.
Strong mathematical proficiency.
Understanding of statistical analysis methods and what they mean.
Knowledge of SQL and a statistical programming language. Python or R preferred, but we'll take SAS, Stata, or S-Plus as long as you can convince us that you could learn any language we need you to learn in a month, and that you'd enjoy that month of your life.
Proficiency in advanced quantitative/statistical modeling and optimization techniques.
Prior experience with business analytics, especially online retail, required.
All-around talent; we want to see that you can think through a complex business problem, offer five ways to solve it, and rough up a couple of the solutions over the next couple of days.
Big Data Analytics
Hadoop related tools (Hive, Pig, Storm, Zookeeper, Kafka, Flume, etc)
Bayesian Statistics/Probabilistic Modeling/Programming
Natural Language Processing
M.S. or Ph.D. in math, physics, computer science, or related field. We're mostly interested in a demonstrated ability and desire to learn and keep learning. Years of experience as a successful data scientist can substitute for an advanced degree.
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