Data Science Manager, Monetization Platform

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

At Stripe, data science managers grow teams and inspire them to rigorous work that shapes our decisions and products. We’re looking for an experienced data science manager to lead our team supporting Stripe’s Monetization team.

As a platform company powering businesses all over the world, Stripe processes payments, runs marketplaces, and detects fraud, helping entrepreneurs start an internet business from anywhere in the world. Monetization Platform provides a central entry point to Stripe teams for monetizing products in global markets, building the platforms that allow us to sell, price, collect revenue, report, and derive insights, across our entire product suite. Data Science is core to this effort, from anomaly detection to ensure accurate billing, to working with engineers on ML-based cost estimation models, to ensuring that we have the right metrics and observability to rigorously measure our performance, we help to make these critical systems understandable, reliable and adaptive.

You will:

• Lead a team of data scientists and analysts to:
• Define and measure key outcome metrics for our systems
• Build models to detect and explain cost and billing anomalies
• Work closely with engineers, product managers and operations to identify important questions and answer them with data
• Partner closely with product and engineering teams to identify and prioritize the most important data science projects
• Recruit great data scientists and analysts, in collaboration with Stripe’s recruiting team
• Develop data scientists and analysts on the team, helping them advance in their careers, providing them with continuous feedback

You’d ideally have:

• 4+ years of data science experience; 1+ years of management experience 
• A PhD or MS in a quantitative field (e.g., Statistics, Economics, Sciences, Mathematics, Engineering)
• Expert knowledge of a scientific computing language (such as R or Python) and SQL
• Strong knowledge of statistics and machine learning
• Experience building data pipelines and working with data processing systems like Spark
• Ability to communicate results clearly and a focus on driving impact