Data from Second Measure drives core strategic decisions for our clients: how to invest multi-billion dollar portfolios, how to allocate nine-figure marketing budgets, and how to prioritize large partnerships and acquisitions.
Through our self-service platform, our clients get instant visibility into any consumer company — how quickly they’re growing, whether they’re gaining or losing share in key markets, how well they retain customers, how much those customers spend over their lifetime, and even where else those customers shop.
This is game-changing data they have never seen before. However, a lot of work goes into making it both useful and accurate.
We’re looking for a data scientist to join our Applied Data team — a multi-disciplinary team of data scientists, engineers, and (soon) designers focused on making our data actionable for our end users. We build the self-service platform through which our clients interact with large-scale behavioral data — e.g., billions of credit card transactions — to answer their own questions in real-time.
In this role, you’ll:
Extend our self-service analytics platform with new metrics and analyses.
Develop novel, generalized methods to address real-world questions. For example:
Is [HelloFresh] growing the overall [meal-kit] market, or simply stealing customers from its competitors?
When customers stop dining at [Chipotle], where do they eat instead?
How much more likely is a[n Equinox] customer in [San Francisco] to shop at [Lululemon], as compared with the average resident?
Design, prototype, and implement new visualizations.
Lead and/or participate in method reviews.
You have a PhD in a quantitative discipline and/or years of experience as a data scientist.
You’re an expert in applied statistics and/or machine learning.
You’re proficient in statistical computing.
You have expertise in panel data, behavioral analysis, text processing, geolocation, and/or time series analysis.
You have experience in large-scale, data-rich environments.
You’ve built things with Python/Scala, Spark, Jupyter, Redshift/Hive, Athena/Presto, GPUs/TPUs, and/or D3 (and related).