Experimentation Lead - Data Science
Posted on Feb 11, 2019 by Indeed
As the world's number 1 job site, our mission is to help people get jobs. We need talented, passionate people working together to make this happen. We are looking to grow our teams with people who share our energy and enthusiasm for creating the best experience for job seekers.
We are a rapidly growing and highly capable engineering team building the most popular job site on the planet. Every month, over 250 million people count on us to help them find jobs, publish their resumes, process their job applications, and connect them to qualified candidates for their job openings. With engineering hubs in Seattle, San Francisco, Austin, Tokyo and Hyderabad, we are improving people's lives all around the world, one job at a time.
Data-informed decision-making is one of our core principles. To do that well, we need data scientists with strong product sensibilities to work alongside our product managers and technical leads.
Are those data points outliers, or does the author just not like the A/B test result? Did the author properly account for the interaction between overlapping tests? Does that unexpectedly skewed distribution indicate a selection bias in the product's users, or did the user interface inadvertently guide users' behavior? Do we know exactly what we are testing and under what conditions we will rollout this test? What is the experimental unit? How are experimental units assigned to groups? Will this experiment be sufficiently powered? These are just a few of the questions you can expect to help answer as a Experimentation Lead on the Data Science team.
- Conceive of and develop methods to establish and massively scale experimentation across the organization by building tools, introducing new techniques, or other means
- Lead organizational educational initiatives to cultivate better A/B testing practices
- Research and develop predictive algorithms which drive metrics/measurement for our tests
- Investigate experiment data for actionable insights about our customers
- Teach statistical inference to audiences with a variety of technical and statistical backgrounds
- Help people get jobs!
If you have an insatiable curiosity, are prone to asking lots of questions, routinely look for quantitative evidence to support or refute qualitative observations and hypotheses, are interested in guiding the direction of products using the best possible data-driven decision-making, and have a passion for helping hundreds of millions of jobseekers find their next job … we'd love to talk with you.
- Bachelor of Arts or Science in a quantitative field (Computer Science, Mathematics, Statistics, Bioinformatics, etc.). Advanced degrees preferred.
- Experiment design - Deep understanding of experiment design: control vs variations, factorial experiment design, multivariate experiments, simple random sampling vs stratified, etc.
- 2+ years industry experience using statistical modeling and/or machine learning techniques to evaluate product performance.
- Strong cross-functional communication skills - The methods we develop are central to how the business runs. So it's critical for you to understand the drivers of our business deeply, and to be able to explain your approach and its business sense
- Autonomy - You will have an enormous amount of latitude to research and develop new techniques and algorithms to increase effectiveness of our experimentation systems and reduce risk. You should be able to use it wisely.
- Experience using statistical techniques to develop experiments that produce actionable insights and trustworthy conclusions.
- Fluency in Python, R, or another scripting language
- 2+ years industry experience pulling data from disparate data sources using query languages (e.g., SQL) and building visualizations that expose the health and performance of products.
- Have experience building or want to build tools that make it easy for non-experts to make decisions with data
- Understand the nuances of both frequentist and Bayesian statistics but are comfortable using the right approach for the problem at hand
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