Sr Data Scientist

PayPal•Chicago, IL

1d•$142,210 - $221,500•Hybrid

About The Position

PayPal, Inc. seeks Sr Data Scientist in Chicago, IL. Apply advanced statistical techniques and algorithms on complex datasets to build statistical or financial models for prediction of risk and revenue and driving actionable business insights for Bill Me Later, Inc. (BML) product or customer level credit risk management. Mine data covering a wide range of information from user profile to transaction details to solve risk problems that involve classification, clustering, pattern analysis, sampling, simulations. Design and refine a valuations framework that enables PayPal to extend credit with a comprehensive view of each decision's NPV. Work with product, data science, and finance teams to enhance underwriting and optimize credit portfolio performance. Identify emerging risks or opportunities and propose mitigation strategies. Identify optimal data sources and process improvements to enhance risk management, customer experience, and growth. Establish detailed KPI tracking to clearly monitor the credit card program health and simplify insights for stakeholders and senior management. Present regular updates to senior management on business health, highlighting successes, challenges, and actionable insights, along with plans for addressing key issues. Partial telecommuting permitted from within a commutable distance.

Requirements

Master’s degree, or foreign equivalent, in Computer Science, Economics, Mathematics, Statistics, Engineering, Business Administration, or other quantitative field, plus three years of experience in credit risk analytics or strategy for consumer lending.
Experience in forecasting techniques/algorithms and automation with Python (2 years)
Machine learning (model development/deployment; optimization including parameter tuning, dimension reduction, feature selection, and model validation)
Predictive analytics and segmentation (classification models, regression models, and statistical analysis)
Experimental design (A/B testing)
Profit & Loss (P&L) analytics
Credit risk management / credit risk analytics
Credit bureau analytics
Python (pandas, numpy, and sklearn packages)
SQL tools: Google Big Query and Teradata
Data visualization (Tableau, Amplitude, and Q-monitor)
Database management: Big data and cloud (Hadoop, Hive, Stampy, Teradata, and Google Big Query)

Responsibilities

Apply advanced statistical techniques and algorithms on complex datasets to build statistical or financial models for prediction of risk and revenue and driving actionable business insights for Bill Me Later, Inc. (BML) product or customer level credit risk management.
Mine data covering a wide range of information from user profile to transaction details to solve risk problems that involve classification, clustering, pattern analysis, sampling, simulations.
Design and refine a valuations framework that enables PayPal to extend credit with a comprehensive view of each decision's NPV.
Work with product, data science, and finance teams to enhance underwriting and optimize credit portfolio performance.
Identify emerging risks or opportunities and propose mitigation strategies.
Identify optimal data sources and process improvements to enhance risk management, customer experience, and growth.
Establish detailed KPI tracking to clearly monitor the credit card program health and simplify insights for stakeholders and senior management.
Present regular updates to senior management on business health, highlighting successes, challenges, and actionable insights, along with plans for addressing key issues.