Lead Data Scientist, Telematics

This is a fully remote position, open to applicants in United States.

📋 Description

• Create and sustain telematics pricing and underwriting models.

• Identify issues, develop datasets, engineer features, construct statistical models, assess models, validate with stakeholders, ensure compliance with regulatory requirements, and monitor performance after deployment.

• Develop and manage dependable modeling datasets, data pipelines, and transformations.

• Verify data quality and lineage.

• Implement models into production and resolve pipeline and system issues.

• Enhance the reliability and maintainability of the Telematics data ecosystem.

• Oversee the complete telematics model lifecycle from raw data ingestion to development, deployment in production, monitoring, and operational support.

• Collaborate with Data Science, Data Engineering, Software Engineering, Pricing, Actuarial, Product, and Compliance teams.

• Introduce innovative statistical algorithms and modeling techniques; prototype, test, retest, and scale methodologies.

• Adopt and enhance modeling best practices, documentation standards, and peer reviews.

• Offer modeling support, documentation, and explainability to state regulators.

• Ensure that statistical models adhere to state-specific regulatory requirements and constraints.

⛳️ Requirements

• Advanced degree in a quantitative field and/or 5+ years of experience applying advanced quantitative techniques to industry challenges.

• Profound 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 abilities in Python and SQL.

• Proven experience in building and maintaining production-quality data pipelines, modeling datasets, and data transformations.

• Familiarity with version control, automated testing, code review, CI/CD, and cloud-based data or machine learning systems.

• Experience in validating data quality, lineage, completeness, consistency, and schema stability.

• Capability to troubleshoot issues across data, application, pipeline, and infrastructure layers.

• Experience in deploying, monitoring, and maintaining production machine learning models.

• Solid understanding of statistical modeling, machine learning, and numerical optimization.

• Excellent data visualization and communication skills.

• Demonstrated experience in building, validating, and applying statistical machine learning methods to real-world challenges.

• Experience using version control systems like Git and cloud computing platforms such as AWS.

• Ability to articulate functional problem statements for the upcoming 1–2 months and make informed decisions within a well-defined problem space.

• Willingness to appear on camera for virtual interviews.

• Preferred but not mandatory: PhD; knowledge of neural networks, survival analysis, causal inference, or Bayesian modeling; experience in insurance; familiarity with insurance concepts such as loss ratios, loss cost, and claims frequency.

🏝️ Benefits

• Competitive bonus structure.

• Equity offering available.

• Flexible work location across the US.

• Reasonable accommodations for qualified applicants with disabilities.