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Data Scientist (Flex Pay)
Skills
SQL QueriesTableauPythonData ScienceData VisualizationNumPypandasData AnalysisMachine LearningAIPySparkPower BI
21 days ago
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Important information
Contract type:
Permanent
Salary:
$100,000 – $120,000 / year
Location:
San Francisco, CA, USA
Work mode:
Hybrid
Published on:
2 October 2026
What they need
- 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
Benefits
- Stock option plan
- 100% paid coverage of medical, dental and vision insurance
- Flexible PTO
- Opportunities for professional growth and development
- Paid parental leave
- Health & wellness initiatives- Advanced Degree (MS/PhD) in Data Science, Statistics, Mathematics, Computer Science, Finance, or a related quantitative discipline
- 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
- 2 years of hands-on experience in a data science or analytics role, preferably in financial services
- Experience with data visualization tools (e.g., Tableau, Power BI)
- Experience with AI tools such as Claude
- Understanding of financial services concepts such as credit scoring, portfolio risk, or customer lifetime value
- Experience with data technologies like PySpark
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