# Data Scientist

**Company:** [Findigs](https://hotfix.jobs/companies/findigs)
**Location:** New York, NY
**Role:** Data Science
**Salary:** $160k – $185k/yr
**Experience:** 4+ years
**Skills:** Python, pandas, scikit-learn, Statsmodels, SQL, dbt, Snowflake, A/B Testing, Supervised Learning, Classification, Regression, Tree-Based Methods
**Posted:** 2026-06-18

> Own feature engineering, model iteration, and A/B testing for an AI underwriting engine that influences rental decisions. Build production risk models and partner with Product and Engineering on high-stakes decisioning systems.

## Job Description

## Where you will make an impact

**DecisionAssist model development:** Own feature engineering, model iteration, and evaluation for DecisionAssist. Work across two surfaces: (1) operational model work in the DA/CAV1 serving layer, and (2) analytics-focused modeling in Snowflake for experimentation and research. Partner with Product and Engineering on what signals matter and why.

**Experimentation and A/B testing:** Design and analyze experiments across underwriting, renter-facing, and PMC-facing product changes, and bring statistical rigor and clear recommendations.

**Predictive and risk modeling:** Build and maintain models used in screening logic (e.g., delinquency risk, income estimation, fraud signals).

**ML infrastructure:** Write clean Python, work in dbt, and operate in a modern data stack.

**Research and analysis:** Tackle high-impact, ad-hoc questions from Product and Customer teams; e.g., what’s driving approval-rate variance, which cohorts behave differently, and what a given signal actually predicts.

## We’d love to hear from you if you have

- 4+ years of hands-on data science or applied ML experience (fintech, proptech, or other high-stakes decisioning environments preferred)
- Strong Python skills (pandas, scikit-learn, statsmodels or equivalent)
- Ability to design, run, and interpret A/B tests independently
- Strong SQL skills and comfort working in a modern data stack (dbt, Snowflake, Sigma, or similar)
- Solid grounding in supervised learning fundamentals (classification, regression, tree-based methods)
- Strong written communication and the ability to explain model behavior and tradeoffs to non-technical partners (e.g., PMs, CSMs)
- Intellectual curiosity about housing and credit data in particular

## Nice-to-haves

- Experience building or contributing to a credit, risk, or underwriting model in production
- Familiarity with fair lending / disparate impact considerations in ML
- Experience working on systems where model output directly affects real people, with a strong sense of responsibility and rigor
- Ability to move between exploratory research and production-grade work without needing separate tracks
- LLM experience (fine-tuning, retrieval, or integration)
- Startup / scale-up experience

## What we offer

- Competitive base salary + Pre-IPO equity
- Unlimited Paid Time Off (PTO) policy
- Health benefits, 401(k) matching up to 4%, monthly gym stipend, and lunch provided every day

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