- Company Name
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- Job Title
- Senior Data Scientist - Finance Management in San Francisco
- Job Description
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**Job Title**
Senior Data Scientist – Finance Management
**Role Summary**
Lead the development of earned wage access (EWA) repayment risk models, from concept to production. Own end‑to‑end data pipelines, feature engineering, and model monitoring to deliver a zero‑to‑one risk scoring framework that surpasses industry benchmarks. Drive insights from large-scale transactional data and translate them into partner‑facing strategies and product enhancements.
**Expectations**
- 5+ years of applied analytics, decision science, or risk modeling, ideally in credit, lending, or payments.
- Proven ownership of projects with measurable business or customer impact.
- Advanced degree (or equivalent experience) in Statistics, Economics, Mathematics, Data Science, or a related field.
**Key Responsibilities**
- Design, build, and validate EWA repayment risk models, focusing on predictive accuracy and actionable behavioral signals.
- Develop scalable data pipelines (e.g., dbt) to automate ETL of transaction, balance, and repayment datasets.
- Create dashboards, alerts, and KPI dashboards to monitor model performance and product impact in production.
- Conduct experimental testing of new modeling approaches and feature sets tailored to short‑term liquidity.
- Partner with product, engineering, and customer teams to prototype, iterate, and deploy risk solutions; translate insights into repayment strategies.
- Document model lifecycle, data lineage, and performance metrics.
- Mentor junior team members and promote best practices in data science and risk modeling.
**Required Skills**
- Proficiency in SQL, Python, and data visualization/analysis tools (e.g., Tableau, Looker, or equivalent).
- Experience with model evaluation, calibration, monitoring, and feature development for credit or risk models.
- Strong analytical capabilities with large financial datasets (transactions, balances, repayment behavior).
- Knowledge of statistical and machine learning techniques (e.g., logistic regression, gradient boosting, survival analysis).
- Familiarity with data pipeline technologies (dbt, Airflow, Prefect).
- Excellent communication skills to collaborate with technical and non‑technical stakeholders.
**Required Education & Certifications**
- Advanced degree (Master’s or Ph.D.) in Statistics, Economics, Mathematics, Data Science, or a closely related field; or equivalent professional experience.
- Relevant certifications (e.g., SAS, Microsoft Certified: Azure Data Scientist Associate, or equivalent) are a plus but not mandatory.
San francisco, United states
On site
Senior
03-11-2025