Staff Data Scientist, ML Systems & Pricing (Equity)

Evolve; Northern (KY); Hybrid; USD 180,000 - 195,000; Full time

Benefits offered by this job

Equity in company

401(k) with match

Parental leave

Health, dental, vision

Generous PTO

Travel credit

Job summary

Evolve’s Data Product team builds data and ML-powered products to improve owner acquisition, pricing, and revenue decisions. We’re hiring a hands-on Staff Data Scientist with applied ML engineering depth to lead high‑impact work across that portfolio.

You’ll turn ambiguous opportunities into production systems: frame problems, develop models, build pipelines, and measure business impact. This role is individual‑contributor, with end‑to‑end ownership and cross‑functional collaboration.

Qualifications

• 6+ years applying data science, statistics, econometrics, or ML to business problems.
• Staff-level scope: led cross-functional DS/ML work from idea to production.
• Experience with production ML systems and deployment/monitoring.

Responsibilities

• Lead the technical direction and hands-on delivery of applied ML initiatives.
• Translate opportunities into decision frameworks, success metrics, and adoption plans.
• Build and operate ML systems end to end: pipelines, training, inference, deployment, monitoring.
• Contribute to forecasting, pricing, and optimization work.
• Design experiments to evaluate product and model changes.
• Set standards for testing, reproducibility, and documentation; review designs and code.
• Partner with product and business to integrate models into workflows.
• Mentor data scientists and maintain hands-on contribution.

Skills

Python

SQL

Production ML systems

Experimentation/Causal inference

Business communication

Leadership

Education

Advanced degree in quantitative field

Tools

Snowflake

dbt

Cloud orchestration

MLOps

Job description

Evolve’s Data Product team builds data and ML-powered products to improve owner acquisition, pricing, and revenue decisions. We’re hiring a hands-on Staff Data Scientist with applied ML engineering depth to lead high‑impact work across that portfolio.

You’ll turn ambiguous opportunities into production systems: frame problems, develop models, build pipelines, and measure business impact. This role is individual‑contributor, with end‑to‑end ownership and cross‑functional collaboration.