Senior Data Scientist

PayPal•Chicago, IL

4d•$153,317 - $221,500•Hybrid

About The Position

PayPal, Inc. seeks Senior Data Scientist in Chicago, IL. This role involves leading the development and implementation of advanced data science models, designing and implementing core decision models for identity, onboarding, authentication, abuse, scam, and product-specific models using Python, SQL, and BigQuery. The position requires collaboration with stakeholders and cross-functional teams (engineers, operations, product) to integrate fraud prediction models and strategies. The Senior Data Scientist will drive best practices in data science, applying statistics, machine learning, and AI to fraud detection, aiming to maintain loss targets while optimizing risk experience and ensuring customer safety. Responsibilities include ensuring data quality and integrity, validating data, mapping discrepancies, and mentoring junior data scientists. The role also involves staying updated with the latest data science trends, exploring advanced technologies like AI, ML, and LLM to enhance risk strategies and modeling against fraud. Partial telecommuting is permitted.

Requirements

Master’s degree, or foreign equivalent, in Computer Science, Engineering, Natural and Applied Science or a closely related field plus two years of experience in the job offered or a related occupation
OR Bachelor’s degree, or foreign equivalent, in Computer Science, Engineering, Natural and Applied Science or a closely related field plus five years of experience in the job offered or a related occupation.
Applying machine learning algorithms on financial data for fraud detection, abuse detection, identity risk or risk assessment, using supervised learning and unsupervised learning (logistic regression, gradient boosting, random forest, clustering) (2 years)
Developing, training, calibrating and validating regression-based default models for fraud or risk prediction (2 years)
Conducting analysis using SQL/SAS/Python in database/server for large-scale data analysis (2 years)
Performing data manipulation and processing using distributed computing frameworks such as PySpark/Spark for feature engineering, model training, or analytics (2 years)
Building scalable data pipelines for analytics or model training using Python, SQL, or cloud-based tools (2 years)
Developing, testing, and operating model training or scoring pipelines in Python on cloud environments using distributed computing clusters (2 years)
Building and deploying automated dashboards and benchmarks for fraud or risk related metrics monitoring using Python, Tableau, and Snowflake (2 years)
Deploying analytical or machine learning models into production and maintain key documentation in production environment on cloud (2 years)
Analyzing transactional or behavior data to identify anomalies, patterns or potential fraud indicators using statistical or machine learning methods (2 years)
Working with cross-functional teams including risk, engineering, and product partner, to translate fraud business requirements into analytical or model solutions (2 years)
Communicating analytical findings, model insights, or fraud trends to different stakeholders (2 years)
Must be legally authorized to work in the U.S. without sponsorship.

Nice To Haves

Partial telecommuting permitted from within a commutable distance.

Responsibilities

Lead the development and implementation of advanced data science models.
Design and implement core decision models for identity, onboarding, authentication, abuse, scam, product-specific models by leveraging Python, SQL languages, and BigQuery tool to design and implement risk decision.
Collaborate with stakeholders to understand requirements.
Work closely with cross-functional teams, including engineers, operations, and product teams, to integrate fraud prediction models and strategies into various systems and processes.
Drive best practices in data science.
Drive success through data-driven approach by applying statistics, machine learning and AI into fraud detection space.
Maintain loss within targets while still delivering best-in-class risk experience by optimizing risk frictions and ensuring PayPal customers are kept safe and ensure through machine learning and AI applications.
Ensure data quality and integrity in all processes.
Ensure data integrity and consistency by working closely with business stakeholders and engineers to address critical data challenges.
Validate the underlying data and map out the discrepancies in our system to help improve data quality and integrity by working with data engineering team.
Mentor and guide junior data scientists.
Provide support to guide junior data scientists in the same team to help them deliver the projects and bridge the knowledge gap.
Stay updated with the latest trends in data science.
Explore most advanced technologies (AI, ML, LLM) to evolve risk strategies and risk modelling to better combat fraud.