Job description

Root was founded on the belief that car insurance is broken, and we set out to change it. We’re harnessing the power of technology to revolutionize this archaic, complicated industry. Using machine learning and mobile telematic platforms, we’ve built one of the most innovative insurtech companies in the world. 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 Lead Data Scientist I to support the Telematics Data Science team. This is a hands-on role focused primarily on developing and maintaining telematics pricing and underwriting models, with a significant secondary responsibility for the data engineering and production systems that support those models. The role is expected to spend approximately 65% of its time on Data Science and 35% on Data Engineering. Data Science responsibilities include problem definition, dataset development, feature engineering, statistical modeling, model evaluation, stakeholder validation, regulatory support, and post-deployment monitoring. Data Engineering responsibilities include building and maintaining reliable modeling datasets, improving data pipelines and transformations, validating data quality and lineage, productionizing models, troubleshooting pipeline and system issues, and improving the reliability and maintainability of the Telematics data ecosystem. This role requires someone who can work across the full lifecycle of a telematics model—from raw data ingestion through model development, production deployment, monitoring, and ongoing operational support. The successful candidate will partner closely with Data Science, Data Engineering, Software Engineering, Pricing, Actuarial, Product, and Compliance teams to deliver reliable and measurable business outcomes. Salary Range : $142,800 - $178,500 (Eligible for competitive bonus and equity offering) Root is a “work where it works best” company. Meaning we will support you working in whatever location that works best for you across the US. We will continue to have our headquarters in Columbus, Ohio. Data Science and Machine Learning • 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. • Familiarity with advanced methods such as neural networks, survival analysis, causal inference, or Bayesian modeling is preferred but not required. Telematics Data Engineering and Production Systems • Demonstrated experience building and maintaining production-quality data pipelines, modeling datasets, and data transformations. • Strong programming skills in Python and SQL, including the ability to write, test, review, and maintain production code. • 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. Product, Pricing and Actuarial Integration • Work closely with Product Management, Pricing and Actuarial teams to ensure alignment and integration between Data science models and Product/Pricing considerations Data Science Leadership and Innovation • Lead innovation by introducing new statistical algorithms and modeling techniques. • Prototype, test, retest and scale approaches. • Adopt and refine modeling best practices, documentation standards, and peer reviews. Compliance and Audit • The statistical models built should comply with state specific regulatory requirements and constraints. • Providing modeling support, documentation and explainability to state regulators. What You Will Need to Succeed • Advanced degree in a quantitative discipline (PhD preferred) and/or 5+ years of applying advanced quantitative techniques to problems in industry • Strong demonstrable knowledge of topics such as statistical modeling, machine learning, and numerical optimization • Exceptional communicator and storyteller with strong data visualization skills • Strong programming skills with experience in SQL and Python • Demonstrated experience building, validating, and applying statistical machine learning methods to real world problems • Demonstrates ownership mentality, taking initiative to find, prioritize, and be accountable for the highest impact work • Ability to frame functional problem statements for the next 1-2 months, consistently making good decisions about the right path to follow in a well-defined problem space • Experience using version control (e.g. Git) and cloud computing (AWS or analogous) • Preferred but not required: • • Experience in one or more insurance lines (P&C, Life, Health, Specialty) • Familiarity/understanding of insurance concepts such as Loss ratios, Loss Cost, Claims frequency etc As part of Root's interview process, we kindly ask that all candidates be on camera for virtual interviews. This helps us create a more personal and engaging experience for both you and our interviewers. Being on camera is a standard requirement for our process and part of how we assess fit and communication style, so we do require it to move forward with any applicant's candidacy. If you have any concerns, feel free to let us know once you are contacted. We’re happy to talk it through. Please see our Privacy Notice available HERE for more information on how we process your personal data. Consistent with the Americans with Disabilities Act (ADA) and the Civil Rights Act of 1964, it is the policy of Root to provide reasonable accommodation when requested by a qualified applicant or candidate with a disability, unless such accommodation would cause an undue hardship for Root. The policy regarding requests for reasonable accommodation applies to all aspects of the hiring process. If reasonable accommodation is needed, please contact recruiting@joinroot.com.

Requirements

Must-haves

Root is a “work where it works best” company. Meaning we will support you working in whatever location that works best for you across the US. We will continue to have our headquarters in Columbus, Ohio.
Data Science and Machine Learning
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.
Telematics Data Engineering and Production Systems
Demonstrated experience building and maintaining production-quality data pipelines, modeling datasets, and data transformations.
Strong programming skills in Python and SQL, including the ability to write, test, review, and maintain production code.
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.
Product, Pricing and Actuarial Integration
Work closely with Product Management, Pricing and Actuarial teams to ensure alignment and integration between Data science models and Product/Pricing considerations
Data Science Leadership and Innovation
Lead innovation by introducing new statistical algorithms and modeling techniques.
Prototype, test, retest and scale approaches.
Adopt and refine modeling best practices, documentation standards, and peer reviews.
Compliance and Audit
The statistical models built should comply with state specific regulatory requirements and constraints.
Providing modeling support, documentation and explainability to state regulators.
What You Will Need to Succeed
Strong demonstrable knowledge of topics such as statistical modeling, machine learning, and numerical optimization
Exceptional communicator and storyteller with strong data visualization skills

Nice-to-haves

Familiarity with advanced methods such as neural networks, survival analysis, causal inference, or Bayesian modeling is preferred but not required.
Advanced degree in a quantitative discipline (PhD preferred) and/or 5+ years of applying advanced quantitative techniques to problems in industry

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningpythonsqlci/cdaws

Platform: rippling
Effort: medium