AI Product & Technical Lead

ParetoHealth•Salt Lake City, UT

9h•Hybrid

About The Position

We’re in this for the greater good at ParetoHealth. Our mission is collective greatness, nothing less will do. Our team is a single force united in the drive to transform employee health benefits. The company was founded in 2011 to help small and medium-sized businesses fight the rising cost of employee health benefits. We blazed the trail with financing innovations that reduce the risks in self-insurance and deliver significant savings—and we continue to lead with a growing ecosystem of partners and world-class cost control solutions. But success is measured by more than dollars alone and we measure ours by the good that comes from knowing that every client and all their employees can count on effective, affordable healthcare for years to come. Please note that ParetoHealth does not provide employment visa sponsorship for this position. Candidates must be authorized to work in the United States without sponsorship both now or in the future. Position Summary: ParetoHealth is redefining risk management in healthcare. Data and AI directly influence how we select risk, price accurately, deploy capital efficiently, and drive high-quality growth. We are hiring a Director, AI Product & Technical Lead to own high-impact AI systems end-to-end — from problem design through model development, deployment, and measurable business impact. This is a true hybrid leadership role. You will function as both: The AI Product Leader, defining the roadmap and aligning initiatives to financial outcomes The Technical Lead, shaping architecture, contributing hands-on to model development, and ensuring production-grade AI systems Initial Focus In the near term, this role will focus primarily on building and deploying predictive models in: Underwriting — improving risk selection, pricing precision, and submission triage Commercial / Broker Targeting — identifying high-value growth opportunities and improving distribution efficiency Both of these areas have already had some work done, but we are ready to take things to the next level. Over time, the mandate will expand to additional operational and strategic domains as AI becomes embedded more broadly across the organization. You will work alongside other data scientists and engineers, accelerating execution while setting the technical and product bar for AI systems at ParetoHealth. This role has direct visibility with executive leadership and clear accountability to measurable financial outcomes.

Requirements

8–12+ years in AI, Applied ML, Product, or hybrid technical roles, with at least 3 years exhibiting true end-to-end ownership of the full product lifecycle
Demonstrated experience shipping production AI systems that delivered measurable financial impact
Strong hands-on proficiency in Python and modern ML tooling and deep experience with predictive modeling techniques (regression, ensembles, forecasting, optimization)
Experience deploying and operating ML systems in cloud environments (AWS, GCP, or Azure)
Familiarity with model monitoring, retraining pipelines, and production ML best practices
Experience mentoring or technically leading other data scientists or ML engineers

Nice To Haves

Exposure to underwriting, insurance, healthcare, or other risk-based businesses strongly preferred
Experience in high-performance AI consulting or transformation environments is a plus.

Responsibilities

Partner with Underwriting, Actuarial, Commercial, and Executive leadership to identify high-leverage AI opportunities
Translate ambiguous business challenges into structured predictive and decision-system problems
Define objective functions aligned to financial metrics (loss ratio, pricing accuracy, growth efficiency, underwriting throughput)
Determine whether predictive modeling, optimization, or workflow redesign is the appropriate intervention
Build clear business cases and prioritize initiatives based on impact, feasibility, and scalability
Lead and contribute hands-on to the development of predictive models (regression, tree ensembles, forecasting, optimization)
Guide feature engineering, model selection, validation, and evaluation methodology
Collaborate with other data scientists to accelerate modeling velocity and raise technical standards
Ensure models are explainable, stable, and aligned with underwriting and commercial decision workflows
Establish disciplined experimentation and phased rollout approaches
Design production architectures spanning data ingestion, feature pipelines, deployment, and workflow integration.
Implement monitoring frameworks for model drift, performance degradation, and business KPI impact
Establish retraining and evaluation pipelines
Ensure reliability, governance, and explainability appropriate for a risk-bearing organization
Prevent “pilot purgatory” by embedding AI systems directly into operational workflows