Staff Data Scientist

Evolve

4d•$180,000 - $195,000•Remote

About The Position

Evolve's Data Product team builds data and machine-learning products that improve owner acquisition and retention, pricing, revenue performance, and how teams make decisions. We're hiring a hands-on Staff Data Scientist with applied ML engineering depth to lead the highest-impact work across that portfolio. You'll turn ambiguous opportunities into durable production systems: frame the problem and success measures, develop and validate models, build the pipelines and inference patterns needed to run them, integrate them into products or workflows, and measure adoption and business impact. You'll also contribute directly to our economic and pricing work, including forecasting, demand and price-elasticity estimation, causal measurement, and optimization. This is an individual-contributor role for someone who enjoys both modeling and systems work. You'll set technical direction and pragmatic engineering standards for a lean data science team, mentor other contributors, and partner closely with Product, Engineering, Data Engineering, Revenue Management, and business leaders. The exact mix of initiatives will shift with company priorities, but end-to-end ownership and measurable business use will remain constant. This role reports to the head of Data Product.

Requirements

Typically 6+ years applying data science, statistics, econometrics, or machine learning to consequential business problems.
Evidence of Staff-level scope: you have led ambiguous, cross-functional DS or ML work from idea through sustained production use, influenced technical direction beyond one project, and created leverage for other contributors.
Strong Python and SQL skills, including the ability to write tested, maintainable production code and work effectively with large datasets.
Experience owning production ML systems, including deployment or scoring, monitoring, failure handling, operational support, and iteration after launch.
Strong applied-statistics and model-evaluation judgment, including experimentation or causal-inference fundamentals and the ability to contribute to economic or optimization work.
Ability to connect technical decisions to business outcomes and communicate tradeoffs clearly to technical, product, business, and executive audiences.
A self-directed, collaborative working style: you can prioritize across a broad problem space, receive and give direct feedback, influence without formal authority, and remain hands-on in a lean, remote team.

Nice To Haves

An advanced degree in a quantitative field, or equivalent depth demonstrated through applied work.
Demand forecasting, dynamic pricing, revenue management, price-elasticity estimation, econometrics, causal inference, or optimization.
Snowflake, dbt, cloud orchestration, and practical MLOps or model-governance patterns.
Marketplace, travel, hospitality, or another domain where models influence high-frequency operating decisions.
NLP, LLM, voice, or other unstructured-data products.

Responsibilities

Lead the technical direction and hands-on delivery of one or two prioritized applied ML or data science initiatives at a time.
Translate business opportunities into clear decision frameworks, technical approaches, success measures, and plans for adoption and impact evaluation.
Build and operate ML systems end to end, including data and feature pipelines, training and evaluation, batch or online inference, deployment, monitoring, failure handling, and iteration.
Contribute directly to forecasting, demand and price-elasticity estimation, causal measurement, and optimization work that supports pricing and revenue decisions.
Design and analyze experiments and quasi-experiments to evaluate product, model, and policy changes.
Establish reusable standards for testing, reproducibility, versioning, observability, documentation, and responsible model operation; review designs and code across the team.
Partner with Product, Engineering, Data Engineering, Revenue Management, and other business owners to make technical tradeoffs and integrate models into products and operating workflows.
Mentor data scientists, raise the team's technical judgment, and create leverage beyond your own projects while remaining hands-on.