Top Tech Jobs & Startup Jobs in Chicago, IL

Lead the design, development, deployment, monitoring, and improvement of interconnected customer lifetime value models covering conversion, retention, premiums, and claim losses. Serve as a hands-on technical leader, guide modeling strategy, evaluate experiments and business impact, partner with engineering and business teams, support production ML systems, establish technical standards, and coach other data scientists.

Set technical direction for Root’s AI platform, including infrastructure, tooling, shared services, governance, reliability, security, observability, and cost management. Lead cross-team initiatives, guide AI and LLM architecture decisions, establish operating standards, shape technical roadmaps, resolve complex tradeoffs, and mentor engineers and technical leaders across the organization.

Lead development and productionization of telematics pricing and underwriting models. Responsibilities span statistical modeling, feature engineering, dataset and pipeline development, model deployment, monitoring, data quality validation, troubleshooting, regulatory support, and collaboration with pricing, actuarial, product, engineering, and compliance teams.

Leads corporate actuarial activities spanning capital modeling, enterprise risk management, reinsurance, reserving, stress testing, ORSA, and regulatory support. The role develops models, evaluates reinsurance impacts, analyzes reserve adequacy, supports financial strategy, automates actuarial processes, and collaborates with Data Science, Finance, Underwriting, Claims, and Accounting. It also presents technical findings and may mentor junior staff or manage direct reports.

Lead financial data engineering initiatives involving commissions, incentive programs, and backend financial systems. Build scalable, accurate, and reliable data pipelines, platform tooling, partner integrations, analytics infrastructure, and data science infrastructure. Shape technical strategy and roadmaps, collaborate with Finance, Product, Data Science, and Engineering, mentor engineers, contribute code, lead incident response, and establish engineering standards.

Lead and grow AI Platform and AI Enablement engineering teams building infrastructure, tooling, and integrations for AI features. Define AI engineering roadmap with product and data science partners, set standards for reliability, observability, security, and responsible AI, manage and develop engineers, engage in code/design reviews, and coordinate cross-team delivery with Platform and Data teams.

Lead performance marketing data science initiatives focused on lead segmentation, bidding optimization, targeting, and marketing spend allocation. Develop and deploy production-ready machine learning systems, establish research and ML operations roadmaps, build monitoring frameworks, and identify high-impact opportunities. Partner with engineering, marketing, vendors, and executives while mentoring data scientists and machine learning engineers and promoting rigorous, maintainable, and reproducible practices.

Lead the AI Platform team in building reliable, secure, observable infrastructure, shared services, deployment patterns, and operational practices for AI capabilities. Set technical priorities, coordinate cross-team dependencies, guide architecture and production operations, improve developer experience, and coach engineers while contributing to complex technical work.

Lead the architecture and development of Root’s machine learning platform for insurance pricing. Build infrastructure for feature pipelines and stores, reproducible training and orchestration, model registries, serving, validation, observability, and research-to-production workflows. Establish versioned contracts and technical standards, automate workflows using LLMs, write and review critical code, influence cross-team strategy, and mentor senior engineers. Collaborate closely with data scientists and researchers to create scalable, reliable, and reproducible ML capabilities.

Lead technical strategy and execution for build, test, release, and deployment systems. Drive initiatives to improve developer productivity, deployment safety, and platform reliability. Coordinate cross-team projects and migrations, contribute high-impact code, coach engineers, and lead incident response and resilience improvements for shared engineering systems.