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

Upgrade; San Francisco (CA); Hybride; USD 100 000 - 150 000; Plein temps

Avantages offerts par ce poste

Stock options

Medical, dental and vision insurance

401(k) plan

Flexible PTO

Professional growth opportunities

Parental leave

Wellness initiatives

Résumé du poste

Upgrade is seeking a Data Scientist for its Flex Pay BNPL platform in San Francisco. The role focuses on building predictive risk models, optimizing offers/pricing, and delivering insights to guide product, risk mitigation, and customer strategy. It requires strong stats, ML, and programming skills.

The position is based in SF with hybrid work on Wednesdays and Thursdays. You will build and deploy models for credit risk, fraud detection, pricing, and customer segmentation, and collaborate across

Qualifications

• MS/PhD in Data Science or related quantitative field
• 3–5+ years in data science/analytics, preferably financial services
• Experience or strong interest in machine learning techniques
• Strong Python (Pandas, NumPy, Scikit-learn) and SQL
• Ability to document analyses for varied audiences
• Proactive, driven, able to work in a fast-paced environment

Responsabilités

• Build and maintain credit and fraud policy simulators
• Develop models for credit risk, fraud, pricing, segmentation, and marketing attribution
• Validate models to understand performance drivers
• Analyze large structured and unstructured data with SQL and Python
• Stay updated on data science/fintech trends and tools
• Collaborate with risk, marketing, product and engineering to drive data-driven strategies

Description du poste

Upgrade is seeking a Data Scientist for its Flex Pay BNPL platform in San Francisco. The role focuses on building predictive risk models, optimizing offers/pricing, and delivering insights to guide product, risk mitigation, and customer strategy. It requires strong stats, ML, and programming skills.

The position is based in SF with hybrid work on Wednesdays and Thursdays. You will build and deploy models for credit risk, fraud detection, pricing, and customer segmentation, and collaborate across