Senior Data Scientist - FinTech Risk & ML (Hybrid SF)

Uplift; San Francisco (CA); Hybrid; USD 100,000 - 150,000; Full time

Benefits offered by this job

Competitive salary and stock option

Medical, dental, and vision insurance

401(k) and RRSP program

Flexible PTO

Opportunities for professional growth

Paid parental leave

Health & wellness initiatives

Job summary

Shoptalk is seeking a highly analytical Data Scientist to join our Flex Pay BNPL team in San Francisco on a hybrid schedule (Wednesdays and Thursdays).

You will build predictive models for credit risk and pricing, analyze large datasets with Python and SQL, and collaborate across risk, product, and engineering to drive decisions.

Candidates typically hold an MS or PhD in a quantitative field and have 3–5+ years of experience with ML techniques such as random forests or gradient boosting.

Qualifications

• MS/PhD in Data Science, Statistics, Mathematics, Computer Science, Finance or related quantitative field.
• 3–5+ years of hands-on data science experience, preferably in financial services.
• Strong Python and SQL skills with ML experience.
• Ability to communicate analyses to diverse audiences.
• Knowledge of credit risk or fintech domain is a plus.

Responsibilities

• Build and maintain credit and fraud policy simulators.
• Develop and deploy ML models for risk, pricing, and segmentation.
• Validate models and monitor performance.
• Analyze large datasets using SQL and Python.
• Stay updated with data science and fintech trends.
• Collaborate with risk, marketing, product, and engineering teams.

Skills

Python

SQL

Pandas

Numpy

Scikit-learn

Modeling

Documentation

Education

MS/PhD in Data Science or related

Tools

PySpark

Tableau

Power BI

Job description

Shoptalk is seeking a highly analytical Data Scientist to join our Flex Pay BNPL team in San Francisco on a hybrid schedule (Wednesdays and Thursdays).

You will build predictive models for credit risk and pricing, analyze large datasets with Python and SQL, and collaborate across risk, product, and engineering to drive decisions.

Candidates typically hold an MS or PhD in a quantitative field and have 3–5+ years of experience with ML techniques such as random forests or gradient boosting.