Data Scientist

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

Compensation: Up to $205,000 base + bonus + equity

Company Overview

A high-growth consumer fintech and e-commerce platform is building the credit infrastructure powering digital commerce in a large, underserved market. The business has reached profitability, processes hundreds of millions in annual transaction volume, and continues to scale rapidly with strong backing from top-tier investors.

The team is lean, highly technical, and composed of leaders from globally recognized technology and marketplace companies. This is an opportunity to join at a pivotal stage and directly influence core revenue-driving systems.

The Role

As a Staff Data Scientist, you will play a critical role in developing and deploying machine learning models that directly impact the company’s P&L. You’ll work across credit risk, pricing, and marketplace optimization problems, owning the full lifecycle from problem definition through to production.

This is a highly cross-functional role partnering with engineering, product, and leadership to drive data-informed decisions and scalable modeling solutions.

Key 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

Requirements

• 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
• Strong problem-solving skills with the ability to operate in ambiguous, fast-paced environments

Preferred Background

• Advanced degree (PhD or Master’s) in a quantitative field such as Statistics, Mathematics, Economics, or Operations Research
• Experience in fintech, lending, or credit risk modeling
• Exposure to marketplace, pricing, or recommendation systems
• Familiarity with optimization techniques and constrained modeling problems

What Makes This Opportunity Unique

• Direct ownership of models that impact revenue and risk
• High visibility role working closely with senior leadership
• Fast-paced, startup environment with significant autonomy
• Opportunity to shape core data science strategy and systems
• If you’re excited by building high-impact machine learning systems in a fast-moving environment and want to see your work directly drive business outcomes, this is a unique opportunity to do so at scale.