Stripe builds the most powerful and flexible tools for running an internet business. We handle billions of dollars each year and enable millions of users around the world to scale faster and more efficiently by building their businesses on Stripe. More than half of US internet users have purchased something from a Stripe user in the past year.

In this role, you will have the opportunity to lead the technical vision for the future of sales forecasting at Stripe. You’ll design and build automated forecasting systems, develop machine learning and statistical methodologies to predict a broad range of success outcomes for Stripe’s existing and newest users, create a framework for anomaly detection in the case of unusual growth patterns, contribute to new compensation programs using the forecasts and other available data, and communicate insights and recommendations to business and technical audiences. You’ll develop solutions end-to-end, from problem discovery and exploratory analysis, to deploying data products and guiding how these products are used to achieve business impact.

You will:

• Work closely with the sales team to identify opportunities and solve business problems with scalable, automated methods
• Define the roadmap to build an automated, robust and extensible forecasting pipeline, thinking strategically about how to maximize business impact
• Directly contribute to the development of automated prediction systems and infrastructure
• Collaborate with data scientists, analysts, engineers to research and implement innovative forecasting and anomaly detection models
• Analyze large datasets to generate insights and make recommendations

We’re looking for someone who has:

• 5+ years experience working with large datasets to solve business problems, including 3+ years developing machine learning or statistical models
• A PhD or MS in a quantitative field (e.g. Engineering, Statistics, Economics, Natural Sciences)
• Proficiency working with a scientific computing language (such as R or Python) and SQL
• Strong knowledge of time series, statistics, machine learning, and/or deep learning
• Expertise in model development, validation and implementation
• The ability to communicate results clearly and a focus on driving impact

Nice to haves:

• Experience designing and developing statistical modeling or machine learning pipelines
• Prior experience with data-distributed tools (Spark, Hadoop, etc)