Staff Data Scientist, LTV

Root Inc.

. Serve as a senior technical leader for the Lifetime Value team while remaining hands-on throughout exploratory analysis, model development, deployment, monitoring, and production support .

Posted 10/2/2026full-timeRemote • United StatesLead💰 $171,400 - $214,200 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in predictive modeling, statistical analysis, and experimental design, with a strong command of Python and SQL. Capable of leading complex data science initiatives while effectively communicating insights and guiding team members.

Highest-signal resume keywords

Predictive ModelingSurvival AnalysisStatistical ModelingPython ProgrammingMLOps Practices

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Predictive ModelingStatistical ModelingSurvival AnalysisExperimental DesignTime-Series ForecastingModel ValidationData ScienceMachine LearningSQLCausal Inference

Soft Skills

Communication SkillsRelationship-BuildingCoaching

Tools & Technologies

AWSDockerDbtAirflowMetaflowMLflow

Industry Keywords

Customer Lifetime Value ForecastingInsuranceRegulated Financial Products

Tech Stack

Tools & technologies

AirflowAWSCloudDockerPythonSQL

About the role

Key responsibilities & impact

Serve as a senior technical leader for the Lifetime Value team while remaining hands-on throughout exploratory analysis, model development, deployment, monitoring, and production support
Lead complex initiatives across interconnected models predicting customer conversion, retention, future premium, and claim losses
Frame ambiguous modeling problems, evaluate analytical approaches, and refine technical direction
Analyze interactions among component models, diagnose underperformance, and prioritize enhancements based on business value
Design and validate experiments and measurement frameworks with clear success criteria
Assess model performance and business impact after launch
Partner with the team manager on quarterly planning, sequencing, capacity, milestones, and dependencies
Work with machine learning engineers and technology teams to support production deployment of models, simulations, and forecasting workflows
Balance rigor, reliability, interpretability, and delivery speed
Communicate recommendations, risks, and tradeoffs to technical partners, business leaders, and senior decision-makers
Guide and coach other data scientists
Develop reusable methods, tools, and standards that improve data science work across the Lifetime Value team and related Quantitative Science work

Requirements

What you’ll need

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, including time-to-event modeling and censoring
Strong statistical modeling, forecasting, experimental design, and validation skills
Software engineering skill in Python, including modular, tested, well-typed, readable code
Experience maintaining and refactoring a large shared codebase
Experience building and running systems of interacting models, such as ensembles or chained predictions
Deep expertise in Python and SQL
Extensive hands-on experience with modern modeling and experimentation frameworks
Strong command of statistical methods, predictive modeling algorithms, survival analysis, time-series forecasting, experimental design, measurement, and validation
Experience developing and maintaining interconnected production models using MLOps practices, including 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
Track record of influencing priorities and technical direction across related workstreams while remaining accountable for hands-on delivery
Ability to guide technical work, coach data scientists, and establish reusable modeling, experimentation, validation, or reporting practices
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
Experience prototyping new modeling techniques or data science tools
Must be on camera for virtual interviews

Benefits

Comp & perks

Competitive bonus
Equity offering
Work in whatever location works best across the US
Reasonable accommodation throughout the hiring process