Data Scientist

Join Findigs, a company revolutionizing the rental industry with its AI underwriting engine, DecisionAssist. As a Data Scientist, you will play a crucial role in model development, experimentation design, and analysis that directly impacts renter and property manager outcomes. You will work closely with Product and Engineering teams, and have the opportunity to grow into a broader strategic role as the team evolves. Enjoy benefits such as unlimited time off, stock options, and 401(k) matching.

• Ownership of model development, experimentation design, and ML-adjacent analysis for the AI underwriting engine.
• Collaboration with Product and Engineering teams to translate real-world rental risk and behavior into models and experiments.
• Designing and analyzing experiments across underwriting, renter-facing, and PMC-facing product changes, and bringing statistical rigor to recommendations.
• Solid grounding in supervised learning fundamentals (classification, regression, tree-based methods)
• Intellectual curiosity about housing and credit data in particular
• 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)
• Strong written communication and the ability to explain model behavior and tradeoffs to non-technical partners (e.g., PMs, CSMs)
• Strong Python skills (pandas, scikit-learn, statsmodels or equivalent); this is a coding role
• 4+ years of hands-on data science or applied ML experience (fintech, proptech, or other high-stakes decisioning environments preferred)
• 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