Data Scientist

Who We Are

Findigs is on a mission to make renting work for all of us. Renting is one of life’s most critical experiences, yet the process is often slow, opaque, and unfair. We’re changing that by building the first end-to-end platform that turns complex screening into a seamless, high-trust experience for both property managers and renters.

We’re growing fast - fueled by $78M in funding from the investors behind companies like Affirm, Gusto, and Uber. With a data-backed product that allows our customers to make smarter, more predictable decisions, and a team dedicated to transparency and precision, we’re not just improving the rental process; we’re setting the new standard for the entire industry.

We’re aiming to double our impact this year, and we need builders, thinkers, and problem-solvers to help us scale. If you’re ready to modernize one of the most essential industries, we’d love for you to be a part of it.

The Role

Findigs runs an AI underwriting engine (DecisionAssist) that makes or influences thousands of rental decisions every week. As Data Scientist at Findigs, you will strengthen our data science and applied machine learning depth: owning hands-on model development, experimentation design, and ML-adjacent analysis that directly impacts renter and property manager outcomes.

Reporting to the Lead Analytics Engineer, this is a highly technical, high-ownership role for a data scientist who wants to build and improve production models, bring statistical rigor to product decisions, and grow into broader strategic scope as the team evolves. You will partner closely with Product and Engineering to translate real-world rental risk and behavior into models, experiments, and clear insights.

Please note, we are unable to sponsor or take over sponsorship of an employment visa at this time.

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
  
  
  

Interviewing with Us

We're committed to making our interview process as effective and candidate-friendly as possible. We use a tool called Brighthire.ai to record our interviews so that our interviewers can focus entirely on the conversation and not get distracted by taking notes. Please note, if you move forward with the interview process, you'll always have the option to opt out of the recording.

We are an equal opportunity employer and, as such, all applicants will be considered based solely upon merit and directly relevant professional competencies.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Compensation Disclosure As Required By NYC Pay Transparency Law.

Actual compensation packages are based on a wide array of factors unique to each candidate, including but not limited to skill set, years and depth of experience, and the scope of responsibilities in the role. In addition to cash compensation, all full time employees receive an equity compensation package.