[Hiring] Staff Data Scientist @Root Insurance

Role Description

Root is seeking a Staff Data Scientist I to lead the design, development, and oversight of the models that power our customer lifetime value ecosystem. This ecosystem includes hundreds of interdependent models and workflows covering conversion, retention, future premium, and claim losses. Its complexity and business importance require a deeply experienced data scientist who can guide and contribute to the team’s most challenging technical work while partnering directly with machine learning engineers on production deployment.

Lifetime value predictions shape some of Root’s most consequential decisions, driving millions of dollars in marketing investment, informing valuations for key business partnerships, and guiding insurance product decisions.

In this role, you will:

• Guide the technical work of the team’s data scientists.
• Partner closely with machine learning engineers, data and software engineers, and business teams to improve decisions across Marketing, Finance, Product, and Customer Experience.
• Be a hands-on individual contributor on that work.
• Carry broad technical responsibility for the quality and evolution of lifetime value modeling.
• Resolve complex modeling questions and dependencies.
• Evaluate enhancement opportunities and establish practical standards for experimentation, validation, and monitoring.
• Help shape quarterly priorities and longer-term technical direction in partnership with the team manager.

Qualifications

• BS, MS, or PhD in Statistics, Computer Science, Economics, or a 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: you write modular, tested, well-typed, readable code, and you've maintained and refactored a large shared codebase over time.
• Experience building and running systems of interacting models, such as ensembles or chained predictions, with attention to both 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, including statistical methods, predictive modeling algorithms, survival analysis, time-series forecasting, experimental design, measurement, and validation.
• Demonstrated ability to frame ambiguous modeling problems, evaluate technical tradeoffs, prioritize high-value opportunities, and deliver high-quality results.
• Experience developing and maintaining interconnected production models using MLOps practices such as feature stores, training and inference pipelines, workflow orchestration, version control, and post-deployment monitoring.
• Ability to estimate the potential value of modeling initiatives and evaluate model performance and business impact after deployment.
• Strong communication and relationship-building skills, with the ability to connect technical work to business goals and explain modeling decisions, risks, and tradeoffs to varied audiences.
• A track record of influencing priorities and technical direction across related workstreams while remaining accountable for hands-on delivery.
• Demonstrated ability to guide technical work, coach data scientists, and establish reusable modeling, experimentation, validation, or reporting practices that improve quality and decision-making across related workstreams.

Requirements

• 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 machine learning platforms and tools such as AWS, Docker, dbt, Airflow, Metaflow, Step Functions, or MLflow.
• Experience building visualizations, dashboards, or reporting that help technical, business, and senior audiences understand model performance, forecasts, and business impact.
• Experience prototyping new modeling techniques or data science tools and turning successful prototypes into durable improvements in how a team works.

Benefits

• Salary Range: $171,400 - $214,200 (Eligible for competitive bonus and equity offering)
• Work where it works best: Support for working in whatever location that works best across the US.