Staff Data Scientist

PayPal

Hybrid

Quick summary

Work type: Hybrid
Location: Chicago, IL
Salary: $142,210–$221,500 / yr
Posted: 2 days ago
Closes: Jun 18, 2026

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $162k

This role $182k

$97k most similar roles pay here $235k

This role pays more than 69% of similar roles. Most pay $126,800–$198,000 — the shaded band above. At the midpoint, this role pays about $182k versus about $162k for comparable roles.

Based on 240 similar postings.

Employer

About PayPal

PayPal is a leading global digital wallet and online payment system, founded in 1998, that allows individuals and businesses to send, receive, and manage funds securely in over 200 markets.

PayPal currently has 84 open roles on FindRole.

Listed pay typically runs $160,500–$235,826 across 84 roles with salary data.

Most-posted roles

View all roles at PayPal

At a glance

TL;DR · Staff Data Scientist

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Staff Data Scientist position available in the Pricing Optimization team at PayPal’s Chicago office, focusing on driving data-informed pricing decisions. This role involves analyzing large datasets to derive actionable insights through hypothesis testing and regression analysis, developing machine learning models for pricing targets, and collaborating with cross-functional teams to influence strategic pricing initiatives. Candidates must have extensive experience in Python (Pandas, NumPy, Scikit-learn, Statsmodels), SQL, statistical inference, and causal impact modeling techniques such as Propensity Score Matching and Difference-in-Differences. The ideal candidate will also possess expertise in supervised and unsupervised learning methods, large-scale experimentation frameworks, and communicating complex data insights to non-technical stakeholders. This role addresses critical business challenges by optimizing pricing strategies at scale for merchant engagement and margin protection.

Skills

Python Pandas NumPy Scikit-learn Statsmodels SQL Statistical analysis Hypothesis testing Regression analysis Causal impact modeling Propensity Score Matching Difference-in-Differences Supervised learning Unsupervised learning Clustering Dimensionality reduction Hierarchical modeling Bayesian approaches Experimentation frameworks

What you'll do

Lead data science initiatives to drive informed pricing decisions.
Analyze complex datasets and translate findings into actionable business insights.
Conduct statistical analysis, hypothesis testing, and regression for decision-making.
Develop machine learning models to identify optimal pricing targets and strategies.
Design experimentation frameworks to evaluate pricing sensitivity and optimize incentives.

What we're looking for

- 6+ years of experience in data science or related field.
- Proficient in Python (Pandas, NumPy, Scikit-learn, Statsmodels) and SQL for data analysis.
- Expertise in statistical methods including hypothesis testing, regression analysis, and causal impact modeling.
- Experience with pricing science concepts such as elasticity and segmentation.
- Strong background in machine learning techniques like supervised and unsupervised learning.
- Ability to communicate complex findings to non-technical stakeholders and drive business decisions.

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