Data Scientist - PayPal Chicago
Overview
PayPal seeks a Data Scientist in Chicago to lead data analysis and modeling initiatives. This role drives statistical insights across large datasets while collaborating with cross-functional teams. The position focuses on optimizing fraud detection and improving operational efficiency through data-driven strategies.
What You'll Do9
- 1Build predictive models to evaluate credit risk and acquisition opportunities
- 2Design and implement statistical tests to compare analytical approaches
- 3Analyze financial datasets to identify operational inefficiencies
- 4Develop interactive dashboards using Tableau to visualize complex data patterns
- 5Create SQL queries to extract insights from petabyte-scale data warehouses
- 6Apply machine learning techniques to predict customer behavior and fraud patterns
- 7Maintain monitoring systems for fraud detection platforms and alert stakeholders
- 8Present portfolio health metrics to senior leadership with actionable recommendations
- 9Own end-to-end resolution of payment loss issues and decline rate optimization
Requirements9
- 1Master’s degree in Computer Science or related field with 2 years technical experience
- 2Fraud risk management experience in financial services with 2 years
- 3SQL proficiency demonstrated through dashboard creation projects
- 4Tableau certification or experience building interactive financial visualizations
- 5Proficiency in Python for large dataset analysis tasks
- 6Experience with statistical modeling and hypothesis testing frameworks
- 7Background in managing data pipelines for high-volume transaction systems
- 8Familiarity with cloud platforms like AWS for scalable data processing
- 9Strong communication skills for presenting technical findings to non-technical audiences
Salary Insight
$97 - $180k per year
Location
Required Skills
Similar open positions
Explore active roles that match your skills and interests.
SmallArc, Inc
VerifiedLead Data Scientist, Fraud Analytics & ML
Own the fraud detection strategy for a financial services client, reducing fraud losses by 20% within the first year. Lead a team of 5 data scientists and collaborate with engineering to deploy ML models in production. You will work directly with the VP of Risk to align fraud use cases with business goals. This role stands out for its focus on Graph Analytics and AI techniques to uncover hidden fraud rings.
Capital_One
VerifiedData Scientist - Business Cards & Payments Credit Infrastructure Team
Lead statistical modeling and data-driven decisions to drive credit card innovations. Partner with cross-functional teams to build scalable valuation models and analytics tools for billions of customer records. Owner of machine learning pipelines and systemized tooling for financial insights.
Ramp
VerifiedData Scientist Finance
Lead full stack development and model building to transform and expose data for stakeholders. Drive experimental design and best practices. Influence processes and systems to enable scalable decisions. Collaborate with finance teams and data engineering to capture and transform raw data into actionable insights. Shape how companies move and manage billions.
Fint Solutions Inc
VerifiedSenior Data Scientist Fint Solutions Inc
Fint Solutions Inc seeks a Senior Data Scientist to drive analytics and machine learning initiatives at scale. This role will own data strategy and deliver insights that enhance customer experience and business performance across large datasets. The ideal candidate will lead projects that scale impact while fostering cross-functional collaboration.
Visa U.S.A. Inc.
VerifiedSenior Manager Data Science - Visa Post Purchase Platform
Visa seeks a Senior Manager Data Scientist to lead data science initiatives for the Post Purchase Platform. This role drives AI innovation at scale, shaping the digital future of payments through cross-functional leadership and technical excellence.
Best Egg
VerifiedProduct Data Scientist
Lead data science initiatives to improve funnel efficiency and drive business KPIs by translating insights into actionable recommendations. Build AI/ML stack solutions and independently deploy them into production. Collaborate with cross-functional teams to address business challenges.