Data Scientist

Where you will make an impact:

• DecisionAssist model development: Own feature engineering, model iteration, and evaluation for DecisionAssist. You will 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, as well as 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: While you won’t own the warehouse or pipeline architecture, you should be comfortable writing clean Python, working in dbt, and operating 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); this is a coding role
• 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 (important given the real-world consequences of renter screening)
• 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), especially as we automate parts of underwriting and screening workflows
• Startup / scale-up experience

What we offer:

• Location: We operate on a hybrid schedule (3-4x times in-office per week), with core collaboration days on Monday, Tuesday, and Thursday at our NoHo office. 
• Mission-Driven Culture: A collaborative, high-impact workplace where we challenge each other to grow, innovate, and drive meaningful change.
• Competitive Compensation: Competitive base salary + Pre-IPO equity.
• Generous Time Off: We trust our team to manage their own time and workload. That's why we offer a Unlimited Paid Time Off (PTO) policy, allowing you to take the time you need to rest and recharge. We also observe all-company holidays.
• Wellness Perks: Health benefits, 401(k) matching up to 4%, monthly gym stipend, and lunch provided every day.