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Salary: USD 171,400 - 214,200 / annual
Root Insurance is seeking a Staff Data Scientist to lead the design, development, and oversight of models powering the customer lifetime value ecosystem. This role combines hands-on technical leadership with mentorship responsibilities, overseeing hundreds of interdependent models and workflows covering conversion, retention, future premium, and claim losses.
You will serve as a senior technical leader for the Lifetime Value team while remaining deeply involved throughout the modeling lifecycle—from exploratory analysis and model development through deployment, monitoring, and production support. Your work will directly influence Root's most consequential business decisions, driving millions of dollars in marketing investment, informing valuations for key business partnerships, and guiding insurance product decisions.
Key responsibilities include: leading complex initiatives across interconnected predictive models; framing ambiguous modeling problems and refining technical direction as evidence emerges; analyzing interactions among component models and diagnosing underperformance; designing and validating experiments with clear success criteria; partnering with the team manager on quarterly planning and technical sequencing; collaborating with machine learning engineers and technology teams on production deployment; communicating recommendations and tradeoffs to technical and business leaders; and guiding and coaching other data scientists to strengthen their analytical skills and judgment.
Root is a "work where it works best" company, supporting remote work across the US while maintaining headquarters in Columbus, Ohio. The role requires strong communication, relationship-building, and the ability to connect technical work to business goals while remaining accountable for hands-on delivery.
REQUIREMENTS:
- BS, MS, or PhD in Statistics, Computer Science, Economics, or related quantitative field
- 8+ years of experience delivering complex, high-impact data science work, including predictive modeling, experimentation, and business decision support
- Strong survival analysis expertise (time-to-event modeling, censoring), grounded in statistical modeling, forecasting, experimental design, and validation
- Software engineering skill in Python: modular, tested, well-typed, readable code; experience maintaining and refactoring large shared codebases
- Experience building and running systems of interacting models (ensembles, chained predictions) with attention to computational efficiency and clarity
- Deep expertise in Python and SQL with extensive hands-on experience using modern modeling and experimentation frameworks
- Strong command of foundational data science principles: statistical methods, predictive modeling algorithms, survival analysis, time-series forecasting, experimental design, measurement, and validation
- Demonstrated ability to frame ambiguous problems, evaluate technical tradeoffs, prioritize high-value opportunities, and deliver high-quality results
- Experience developing and maintaining interconnected production models using MLOps practices (feature stores, training/inference pipelines, workflow orchestration, version control, post-deployment monitoring)
- Ability to estimate modeling initiative value and evaluate model performance and business impact post-deployment
- Strong communication and relationship-building skills; ability to explain modeling decisions, risks, and tradeoffs to varied audiences
- Track record of influencing priorities and technical direction across workstreams while remaining accountable for hands-on delivery
- Demonstrated ability to guide technical work, coach data scientists, and establish reusable practices that improve quality and decision-making
NICE TO HAVE:
- Familiarity with customer lifetime value forecasting, simulation workflows, forecast-versus-actual analysis, or causal inference
- Experience with insurance or regulated financial products
- Experience with cloud-based data and ML platforms (AWS, Docker, dbt, Airflow, Metaflow, Step Functions, MLflow)
- Experience building visualizations, dashboards, or reporting for technical, business, and senior audiences
- Experience prototyping new modeling techniques or data science tools and scaling successful prototypes
