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FinTech, Online Lending, Financial Services, Banking

Senior Data Scientist - Flex Pay

RemoteRemoteFull Time$100,000–$150,000 /yrPosted 1 month agoVisa sponsorship available

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Required skills

  • credit policy simulators
  • fraud policy simulators
  • statistical models
  • machine learning algorithms
  • credit risk
  • fraud detection
  • pricing
  • customer segmentation
  • marketing attribution
  • model validation

Nice to have

  • Tableau
  • Power BI
  • Claude
  • PySpark
  • credit scoring
  • portfolio risk

Role summary

We are seeking a Data Scientist for our Flex Pay (BNPL) sector. You will build predictive risk models, optimize offers/pricing, and extract insights for product development, risk mitigation, and customer strategy. This role requires strong statistics, machine learning, and programming skills. Responsibilities include building/maintaining policy simulators, deploying ML models for credit risk, fraud, pricing, and segmentation, analyzing large datasets using SQL/Python, and collaborating with cross-functional teams. The position is hybrid in San Francisco.

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.

While this position is ideally based in our San Francisco office on a hybrid basis, we are also open to considering remote candidates.

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.
  • 3-5+ 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-$150,000 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.

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