Lead Data Scientist, Telematics

🔥 28 minutes ago

🇺🇸 United States – Remote

💵 $142.8k - $178.5k / year

⏰ Full Time

🟠 Senior

📊 Data Scientist

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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

• Develop and maintain telematics pricing and underwriting models • Define problems, develop datasets, engineer features, build statistical models, evaluate models, validate with stakeholders, support regulatory requirements, and monitor post-deployment performance • Build and maintain reliable modeling datasets, data pipelines, and transformations • Validate data quality and lineage • Productionize models and troubleshoot pipeline and system issues • Improve the reliability and maintainability of the Telematics data ecosystem • Manage the full telematics model lifecycle from raw data ingestion through development, production deployment, monitoring, and operational support • Partner with Data Science, Data Engineering, Software Engineering, Pricing, Actuarial, Product, and Compliance teams • Introduce new statistical algorithms and modeling techniques; prototype, test, retest, and scale approaches • Adopt and refine modeling best practices, documentation standards, and peer reviews • Provide modeling support, documentation, and explainability to state regulators • Ensure statistical models comply with state-specific regulatory requirements and constraints

🎯 Requirements

• Advanced degree in a quantitative discipline and/or 5+ years of applying advanced quantitative techniques to industry problems • Strong knowledge of statistical modeling and machine learning methods, including GLMs, tree-based models, time-series analysis, feature engineering, model evaluation, resampling, and hyperparameter tuning • Strong programming skills in Python and SQL • Demonstrated experience building and maintaining production-quality data pipelines, modeling datasets, and data transformations • Experience with version control, automated testing, code review, CI/CD, and cloud-based data or machine learning systems • Experience validating data quality, lineage, completeness, consistency, and schema stability • Ability to troubleshoot issues across data, application, pipeline, and infrastructure layers • Experience deploying, monitoring, and maintaining production machine learning models • Strong knowledge of statistical modeling, machine learning, and numerical optimization • Strong data visualization and communication skills • Demonstrated experience building, validating, and applying statistical machine learning methods to real-world problems • Experience using version control such as Git and cloud computing such as AWS • Ability to frame functional problem statements for the next 1–2 months and make decisions in a well-defined problem space • Ability to work on camera for virtual interviews • Preferred but not required: PhD; neural networks, survival analysis, causal inference, or Bayesian modeling; insurance experience; familiarity with insurance concepts such as loss ratios, loss cost, and claims frequency

🏖️ Benefits

• Competitive bonus • Equity offering • Work from any location across the US • Reasonable accommodation for qualified applicants with disabilities