Staff Data Scientist
About The Position
PayPal, Inc. seeks Staff Data Scientist in Chicago, IL. This role will lead Data Science initiatives within the Pricing Optimization team to drive data-informed pricing decisions. The position involves analyzing large, multidimensional datasets, translating findings into actionable insights, and conducting hypothesis testing, statistical inference, and regression analysis to support decision-making. The Staff Data Scientist will manage and derive insights from large, complex, and unstructured datasets, and develop machine learning and statistical models to identify pricing targets and treatment strategies that protect margin and sustain merchant engagement. Additionally, they will design and implement experimentation frameworks to evaluate pricing sensitivity and optimize incentive strategies, and collaborate cross-functionally with Product, Engineering, and Finance teams to influence pricing decisions and roadmap priorities. Partial telecommuting is permitted from within a commutable distance.
Requirements
- Bachelor’s degree, or foreign equivalent, in Data Science, Business Analytics, or a closely related field plus six years of experience in the job offered or a related occupation.
- Experience with scripting languages: Python (Pandas, NumPy, Scikit-learn, and Statsmodels), and SQL, for data manipulation and modeling (5 years)
- Experience working with large, complex, and high-dimensional datasets in a production environment (5 years)
- Experience in statistical analysis, hypothesis testing, and regression (5 years)
- Experience communicating with cross-functional executive stakeholders, driving alignment on high-impact data science initiatives, and influencing non-technical stakeholders to drive business impact (5 years)
- Experience with pricing science concepts, including elasticity, marginal value, and segmentation (2 years)
- Experience with causal impact modeling techniques, including Propensity Score Matching and Difference-in-Differences (2 years)
- Experience with supervised learning (e.g., regression, classification) and unsupervised learning (e.g., clustering, dimensionality reduction) methods (5 years)
- Experience analyzing large, multi-dimensional data sets and synthesizing insights into actionable business insights and solutions (6 years)
- Experience with statistical inference techniques for complex business scenarios, including hierarchical modeling and Bayesian approaches (5 years)
- Experience designing experimentation frameworks and mentoring teams on best practices in large-scale A/B and multivariate testing (2 years)
- Must be legally authorized to work in the U.S. without sponsorship.
Responsibilities
- Lead Data Science initiatives within the Pricing Optimization team to drive data-informed pricing decisions.
- Analyze large, multidimensional datasets and translate findings into actionable insights.
- Conduct hypothesis testing, statistical inference, and regression analysis to support decision-making.
- Manage and derive insights from large, complex, and unstructured datasets.
- 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.
Benefits
- Generous paid time off
- Healthcare coverage for you and your family
- Resources to create financial security
- Support your mental health
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What This Job Offers
Job Type
Full-time
Career Level
Senior
