Staff Data Scientist — Fintech ML for Revenue & Risk (Equity)

Harnham; New York (NY); On-site; USD 123,000 - 205,000; Full time

Job summary

Harnham in New York invites a Staff Data Scientist to build and deploy ML models affecting credit risk, pricing, and marketplace optimization. You will own full lifecycle from problem definition to production, collaborating with engineering, product, and leadership to drive data-informed decisions.

The role demands 5+ years in data science, strong modeling skills, production deployment experience, and proficiency in Python and SQL.

Qualifications

• 5+ years of experience in data science or machine learning in a production environment.
• Strong foundation in statistical modeling and machine learning (e.g., classification, ensemble methods).
• Experience deploying models into production and iterating based on real-world performance.
• Proficiency in Python and SQL; experience with experimentation, causal inference, or uplift modeling.

Responsibilities

• Build and deploy machine learning models for underwriting, credit risk, and portfolio optimization.
• Develop pricing, ranking, and personalization algorithms to improve marketplace performance.
• Apply causal inference and experimentation techniques to optimize decision-making.
• Own projects end-to-end: from exploratory analysis and modeling through to production deployment.
• Translate complex modeling outputs into clear business insights and recommendations.
• Collaborate closely with engineering and product teams to operationalize models.

Skills

Python

SQL

Statistical Modeling

Production ML

Experimentation

Problem Solving

Education

PhD or Masters in a quantitative field

Job description

Harnham in New York invites a Staff Data Scientist to build and deploy ML models affecting credit risk, pricing, and marketplace optimization. You will own full lifecycle from problem definition to production, collaborating with engineering, product, and leadership to drive data-informed decisions.

The role demands 5+ years in data science, strong modeling skills, production deployment experience, and proficiency in Python and SQL.