Staff Data Scientist, LTV

🕒 October 2

🇺🇸 United States – Remote

💵 $171.4k - $214.2k / year

⏰ Full Time

🔴 Lead

📊 Data Scientist

👻 Ghost score 0%

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Root Inc.

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2015

🚘 Automotive

💼 Consulting

🛡️ Insurance

💰 $200M Post IPO debt on 2024-11

Automotive • Consulting • Insurance

Root Inc. is an auto insurance company that uses a mobile app and smartphone sensor telematics to measure driving behavior and price policies primarily based on how safely customers drive. The company offers a "test drive" period to gather driving data, provides in-app quotes, policy management, and claims filing, and sells roadside assistance and other coverages across many U. S. states. Root targets consumer drivers (B2C) seeking usage-based insurance and emphasizes savings for safer drivers.

📋 Description

• 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

• 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

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