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Data Scientist, Risk UX 39 views

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Their mission is to increase the GDP of the internet, and they have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

What you’ll do

Stripe is looking for a talented data scientist to partner with the Risk UX team. You’ll take a rigorous approach to quantify user friction and drive risk/reward trade-offs across partner teams. As a data scientist, you will get to employ the full data science toolbox in order to solve challenging problems in this exciting domain, from experiments to working with the partner teams to rigorously define and forecast metrics, and developing and deploying ML models to solve user needs.

Responsibilities

  • Act as an embedded partner to the Risk UX team, helping them to identify and answer questions with data and modeling
  • Create analyses that tell a story focused on actionable insights, not just data
  • Build statistical and/or machine learning models to quantify user friction
  • Analyze user survey results at a large scale
  • Use data, experiments, statistical inference to measure UX improvements
  • Build and improve data ETL pipelines to collect new data and refine existing data sources

Who you are

Stripe is looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum Requirements

  • A PhD or MS in a quantitative field (preferably in Economics, otherwise Operations Research, Statistics, Sciences, Engineering)
  • Ideally 5+ years experience working with and analyzing large data sets to solve problems and drive impact
  • Expert knowledge of a scientific computing language (such as R or Python) and SQL
  • Strong knowledge of statistics, machine learning and optimization
  • Experience working with multiple cross-functional teams to deliver results

Preferred Qualifications

  • Experience with tools for working with “big data” in a distributed fashion (Spark, Hadoop, etc.)
  • Experience applying Natural Language Processing techniques to large datasets

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