Staff Data Scientist, LTV

Root Insurance

9d•$171,400 - $214,200•Remote

About The Position

The Opportunity We believe that a disruptive insurance company must have a principled quantitative framework at its foundation. At Root, we are committed to the rigorous development and effective deployment of modern statistical machine learning methods to problems in the insurance industry. 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 and partner closely with machine learning engineers, data and software engineers, and business teams to improve decisions across Marketing, Finance, Product, and Customer Experience. You will also be a hands-on individual contributor on that work. This role carries broad technical responsibility for the quality and evolution of lifetime value modeling. You will resolve complex modeling questions and dependencies, evaluate enhancement opportunities, make principled tradeoffs, and establish practical standards for experimentation, validation, and monitoring. In partnership with the team manager, you will help shape quarterly priorities and longer-term technical direction. The ideal candidate combines deep modeling expertise and strong execution with the ability to improve the work of others. You can personally deliver complex analyses and models, exercise sound judgment across a highly complex ML system, and help develop other data scientists.

Requirements

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.

Nice To Haves

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.

Responsibilities

Serve as a senior technical leader for the Lifetime Value team while remaining hands-on throughout the modeling lifecycle, from exploratory analysis and model development through deployment, monitoring, and production support.
Lead complex initiatives across interconnected models that predict customer conversion, retention, future premium, and claim losses. These predictions guide substantial marketing investment and significant product decisions.
Frame ambiguous modeling problems, evaluate analytical approaches, and refine the technical direction as new evidence emerges.
Analyze interactions among component models, diagnose underperformance, and prioritize enhancements based on their potential business value.
Design and validate experiments and measurement frameworks with clear success criteria, and assess model performance and business impact after launch.
Partner with the team manager on quarterly planning and sequencing, considering capacity, milestones, and dependencies while communicating risks and helping remove blockers.
Work with machine learning engineers and technology teams to support the production deployment of models, simulations, and forecasting workflows while balancing rigor, reliability, interpretability, and delivery speed.
Communicate recommendations, risks, and tradeoffs clearly to technical partners, business leaders, and senior decision-makers.
Guide and coach other data scientists, strengthening their analytical skills, technical judgment, and subject-matter expertise through constructive feedback.
Strengthen an inclusive, collaborative team culture and develop reusable methods, tools, and standards that improve data science work across the Lifetime Value team and influence related work across Quantitative Science.