Staff Data Scientist

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

Staff Data Scientist at PayPal in Chicago, IL, focused on leading Data Science initiatives within the Pricing Optimization team. The role involves analyzing large datasets, developing machine learning models for pricing and merchant engagement, designing experimentation frameworks for pricing sensitivity, and collaborating with cross-functional teams to influence pricing decisions. Requires a Bachelor's degree and 6 years of experience, with specific skills in Python, SQL, statistical analysis, pricing science, causal impact modeling, supervised/unsupervised learning, and experimentation design.

What you'd actually do

  1. Lead Data Science initiatives within the Pricing Optimization team to drive data-informed pricing decisions.
  2. Analyze large, multidimensional datasets and translate findings into actionable insights.
  3. Conduct hypothesis testing, statistical inference, and regression analysis to support decision-making.
  4. Manage and derive insights from large, complex, and unstructured datasets.
  5. Develop machine learning and statistical models to identify pricing targets and treatment strategies that protect margin and sustain merchant engagement.

Skills

Required

  • Python (Pandas, NumPy, Scikit-learn, and Statsmodels)
  • SQL
  • statistical analysis
  • hypothesis testing
  • regression
  • pricing science concepts
  • elasticity
  • marginal value
  • segmentation
  • causal impact modeling techniques
  • Propensity Score Matching
  • Difference-in-Differences
  • supervised learning
  • regression
  • classification
  • unsupervised learning
  • clustering
  • dimensionality reduction
  • hierarchical modeling
  • Bayesian approaches
  • experimentation frameworks
  • large-scale A/B and multivariate testing

Nice to have

  • communication with cross-functional executive stakeholders
  • driving alignment on high-impact data science initiatives
  • influencing non-technical stakeholders to drive business impact

What the JD emphasized

  • Develop machine learning and statistical models to identify pricing targets and treatment strategies that protect margin and sustain merchant engagement.
  • Design and implement experimentation frameworks to evaluate pricing sensitivity and optimize incentive strategies.

Other signals

  • Develop machine learning and statistical models to identify pricing targets and treatment strategies that protect margin and sustain merchant engagement.
  • Design and implement experimentation frameworks to evaluate pricing sensitivity and optimize incentive strategies.
  • Collaborate cross-functionally with Product, Engineering, and Finance teams to influence pricing decisions and roadmap priorities.