Data Scientist

Findigs; New York (NY); Hybrid; USD 160,000 - 185,000; Full time

Benefits offered by this job

Unlimited Paid Time Off

Health benefits

401(k) matching

Monthly gym stipend

Lunch provided daily

Job summary

Findigs, located in New York City, is seeking a Data Scientist to enhance our AI underwriting engine, DecisionAssist. You will be responsible for hands-on model development, experimentation design, and translating rental risk into clear insights.

This role requires strong Python and SQL skills, and at least 4 years of experience in data science or applied machine learning. We operate on a hybrid schedule with a competitive salary and numerous benefits, including unlimited PTO.

Qualifications

• 4+ years of hands-on data science or applied ML experience, preferably in fintech or proptech.
• Strong Python skills including pandas and scikit-learn.
• Ability to design and interpret A/B tests independently.
• Strong SQL skills and familiarity with modern data stacks.
• Solid grounding in supervised learning fundamentals.
• Strong written communication skills for explaining complex topics.

Responsibilities

• Develop and evaluate models for DecisionAssist and analytics.
• Design and analyze A/B tests for product changes.
• Build and maintain predictive models.
• Work within the ML infrastructure using Python and dbt.
• Conduct research and provide insights based on data analysis.

Skills

Data Science

Python

SQL

A/B testing

Supervised Learning

Model Evaluation

Tools

Snowflake

dbt

Job description

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.

Responsibilities

• 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.

Qualifications

• 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

Benefits

• 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 an 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.

$160,000 - $185,000 a year

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.

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