Data Scientist - Flex Pay

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

About the Role:

We are seeking a highly analytical and results-driven Data Scientist to join our buy now pay later (BNPL) sector called Flex Pay. You will play a key role in building predictive risk models, optimizing offers and pricing, and extracting insights that drive product development, risk mitigation, and customer strategy. This role requires a strong foundation in statistics, machine learning, and programming.

This position is based in our San Francisco office in a hybrid capacity, specifically on Wednesdays and Thursdays.

What You'll Do:

• 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.

What We Look For:

• Advanced Degree (MS/PhD) in Data Science, Statistics, Mathematics, Computer Science, Finance, or a related quantitative discipline.
• 2 years of hands-on experience in a data science or analytics role, preferably in financial services.
• Experience and/or strong interest in 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 (e.g., technical staff, business leads, etc.).
• Proactive, driven, and ability to work in a fast paced environment.

Nice to Have:

• Experience with data visualization tools (e.g., Tableau, Power BI).
• Experience with AI tools such as Claude
• Experience with data technologies like PySpark.
• Understanding of financial services concepts such as credit scoring, portfolio risk, or customer lifetime value.

What We Offer You:

• Competitive salary and stock option plan
• Paid coverage of medical, dental and vision insurance
• Competitive 401(k) and RRSP program
• Flexible PTO
• Opportunities for professional growth and development
• Paid parental leave
• Health & wellness initiatives

The compensation range of this position in San Francisco, CA is USD $100,000-$120,000 annually plus equity and benefits. Within this range, an individual's base pay will be dependent on a variety of factors, including without limitation, job-related knowledge, skills, education, and experience.