Sr. Data Scientist

PayPal PayPal · Fintech · Chicago, IL +1 · Data Science

Senior Data Scientist at PayPal in Chicago, IL, focused on reducing card losses by developing and optimizing risk strategies throughout the customer lifecycle. This role involves data analytics, developing innovative card risk solutions, and leveraging machine learning models for fraud detection and transaction-level risk alerts. The position requires experience in financial risk, fraud management, A/B testing, SQL, Python, and building risk metrics dashboards.

What you'd actually do

  1. Help reduce Card losses by developing and optimizing risk strategies throughout the customer lifecycle.
  2. Drive business strategy recommendations through data analytics on Card authorization and PayPal customer data.
  3. Develop innovative card risk solutions while collaborating with key stakeholders in core payments, operations, risk, finance, and product owners.
  4. Develop and optimize risk strategies for PayPal, leveraging knowledge of U.S. or international financial markets, card networks and customer spending behavior.
  5. Develop business cases in support of new risk policies, risk vendor integrations, and PayPal risk infrastructure enhancements.

Skills

Required

  • Master's degree, or foreign equivalent, in Engineering, Statistics or a closely related field plus two years of experience
  • analyzing and solving financial risk cases using regression and logistics statistical methods and Python
  • fraud and risk management for debit card end to end lifecycle
  • Construct, implement, and fine-tune risk strategies across debit card platforms
  • Use A/B testing and statistical validation to refine fraud rules
  • developing SQL queries on large-scale financial transaction databases
  • developing machine learning models using Python to predict transaction-level risk alerts and support fraud detection for payment systems
  • Industry experience in payments, e-commerce, or financial services
  • collaborating with product, operation and engineering teams in financial services to investigate and resolve technical problems
  • building and visualizing risk metrics in Tableau and Looker dashboards to monitor approval trends, decline rates and loss ratios
  • setting up ring alert to inform business decisions

What the JD emphasized

  • fraud and risk management for debit card end to end lifecycle
  • developing machine learning models using Python to predict transaction-level risk alerts and support fraud detection for payment systems

Other signals

  • develop and optimize risk strategies
  • develop innovative card risk solutions
  • develop machine learning models to predict transaction-level risk alerts and support fraud detection