Lead Data Scientist

Evolve

full-time

Posted on: 9/26/2025

Origin: • 🇺🇸 United States • California, Colorado, New York

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💰 $141,000 - $184,000 per year

Senior

AirflowAWSCloudEC2PythonSQL

About the role

Serve as a senior technical contributor and scientific lead for churn prediction, Dollars-at-Risk, anomaly detection, and LTV forecasting
Ensure LTV projections are integrated with churn, anomaly, and income signals to deliver a business-ready forecasting tool
Mentor and develop data scientists, providing code reviews, standards, and career guidance
Partner with Product teams to design and evaluate experiments and owner lever interventions
Lead causal inference analyses to understand drivers of churn, retention, and LTV
Oversee deployment and monitoring pipelines, ensuring reproducibility, calibration, and incident readiness
Collaborate with Data Engineering/Infrastructure teams to maintain orchestration (GitLab CI/CD, Snowflake, AWS) and deliver production-ready ML systems
Establish and enforce best practices for modeling, documentation, monitoring, and governance
Effectively communicate complex findings to both technical and non-technical stakeholders
Stay current on advances in ML/AI and bring forward innovative methods applicable to Evolve’s business

7+ years of applied data science experience, including 3+ years leading ML projects in production
Prior leadership and/or mentorship experience with a strong record of code reviews, reproducibility, and standards enforcement
Proven track record of business impact from churn, retention, LTV, or anomaly detection models
Expertise in survival analysis, causal inference, uplift modeling, anomaly detection, and calibration
Strong programming experience with Python and SQL
Experience with ML ops and orchestration (Airflow, GitLab CI/CD)
Familiarity with cloud technologies, especially AWS (S3, EC2, SageMaker)
Ability to partner cross-functionally with Product, Commercial Strategy, and FP&A
Excellent communicator — able to translate complex analyses into actionable business decisions
Bachelor’s degree in Computer Science, Statistics, or related quantitative field (Master’s or PhD preferred)