Data Scientist — Credit Risk & ML (Hybrid SF)

Shoptalk; San Francisco (CA); Hybrid; USD 100,000 - 120,000; Full time

Benefits offered by this job

Competitive salary

Stock options

Health, dental and vision insurance

Flexible PTO

Professional growth opportunities

Parental leave

Wellness programs

Job summary

Shoptalk in San Francisco is seeking a Data Scientist to build predictive risk models for Flex Pay BNPL, optimize offers and pricing, and drive product strategy. The role requires a strong foundation in statistics, ML, and programming, with hybrid work on Wednesdays and Thursdays.

You will develop and validate credit risk and fraud models, analyze large datasets using Python and SQL, and collaborate with risk, marketing, product, and engineering to implement data-driven solutions.

Qualifications

• MS or PhD in Data Science, Statistics, Mathematics, Computer Science, Finance, or a related quantitative discipline.
• 2+ years of hands-on experience in data science or analytics, preferably in financial services.
• Experience with machine learning techniques (Random Forest, Gradient Boosted Trees, etc.) strongly preferred.
• Strong proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL.
• Ability to write documentation and present analysis to people with different levels of expertise.
• Proactive, driven, and able to work in a fast-paced environment.

Responsibilities

• Build and maintain credit and fraud policy simulators used to ensure properly functioning systems and identify risk decisioning enhancements.
• Build and deploy statistical models and machine learning algorithms to solve business problems in areas like credit risk, fraud detection, pricing, customer segmentation, and marketing attribution.
• Validate models to identify factors that may affect model performance.
• Analyze large, structured and unstructured datasets using SQL, Python or similar tools.
• Stay up to date with the latest trends and technologies in data science and fintech, actively research new tools and techniques available for model development.
• Collaborate with cross-functional teams including risk, marketing, product, and engineering to define data-driven strategies.

Skills

Python

SQL

Statistical Modeling

Documentation

Team Collaboration

Fintech Domain

Communication

Education

MS/PhD in Data Science or related

Tools

Pandas

NumPy

Scikit-learn

PySpark

Tableau

Power BI

Claude

Job description

Shoptalk in San Francisco is seeking a Data Scientist to build predictive risk models for Flex Pay BNPL, optimize offers and pricing, and drive product strategy. The role requires a strong foundation in statistics, ML, and programming, with hybrid work on Wednesdays and Thursdays.

You will develop and validate credit risk and fraud models, analyze large datasets using Python and SQL, and collaborate with risk, marketing, product, and engineering to implement data-driven solutions.