About this role
A Product Data Scientist who enjoys bridging the gap between deep analytical rigor and product strategy would find this role suitable. This position is embedded directly with the Product Management team to enhance and grow the Home Improvement lending product. The individual will serve as the analytical foundation for high-stakes decisions concerning underwriting funnels, borrower experience, and overall product growth. This role requires translating complex, often messy, data into clear, actionable evidence, including the development and deployment of models and instrumentation that directly influence product decisions.
This role exists to ensure that product decisions are grounded in robust experimentation and sound statistical analysis. The Data Scientist will be responsible for designing and interpreting rigorous A/B tests across the entire borrower journey, defining the core metrics framework, and ensuring data pipelines are reliable. Furthermore, the role integrates modern AI fluency, utilizing LLMs and agentic tooling to accelerate analysis while maintaining critical human oversight. It is ideal for someone who can operate effectively across statistical modeling, data engineering fundamentals, and cross-functional communication.
What you will do
- Partner with Home Improvement Product Managers to convert open-ended product questions into structured analyses and actionable recommendations.
- Design, execute, and interpret experiments, including A/B and quasi-experimental tests, across the borrower funnel to assess impact.
- Define and own the metrics framework for the product line, specifying north-star metrics, guardrail metrics, and funnel definitions.
- Collaborate with engineering to ensure event tracking and logging are complete, accurate, and properly documented.
- Build and maintain data pipelines and models, such as SQL/dbt transformations and feature pipelines, to support self-sufficient analysis.
- Develop and validate statistical and machine learning models, including propensity models and funnel drop-off models, in partnership with credit strategy.
- Apply AI fluency to accelerate exploratory analysis and documentation, ensuring human judgment validates automated outputs.
- Communicate findings clearly through written reports, visualizations, and recommendations tied directly to product roadmaps.
What you need
- Possess at least five years of experience in a hybrid analytics/data science role, such as product data science or analytics consulting, with a proven history of informing product decisions.
- Must have a strong foundation in statistics and experimentation, including hypothesis testing, causal inference, and experiment design.
- Fluency in SQL and proficiency in at least one scripting or statistical language, such as Python or R, is required.
- Demonstrated comfort with data engineering fundamentals, including pipelines, transformations, and data modeling, to build necessary analytical solutions.
- Ability to develop, validate, and communicate the trade-offs between statistical and machine learning models, understanding when simpler methods are more appropriate.
- Experience using AI tools for exploratory analysis and documentation, with a clear understanding of when to apply human scrutiny.
- Must possess consultant-like thinking to structure ambiguous problems and deliver recommendations stakeholders can act upon.
- Must maintain rigor in measurement integrity, balancing the need for speed with the necessity of accurate data analysis.
Nice to have
- Background in fintech
- Background in consumer lending
- Background in home improvement/contractor financing
- Experience with CDP platforms
- Experience with event instrumentation tooling (e.g., Segment, mParticle, Amplitude)
Benefits
- Remote
Skills and tech stack
Questions candidates ask
What is the remote work policy?
The job location is listed as Mexico, and the mode of work is remote. This indicates the position is set up to be performed remotely.
What is the compensation structure?
The posting does not specify salary details for this role.
What is the primary tech stack used?
The required technical skills include Python or R, SQL, and experience with dbt transformations for data modeling.
What kind of team will I be working with?
The role is embedded directly with the Product Management team, serving as their embedded data science and analytics resource.
How is growth handled?
The role involves contributing to PI planning and roadmap discussions by sizing opportunities and flagging measurement risk in proposed initiatives.