Gradient AI is revolutionizing Group Health and P&C insurance with AI-powered solutions that help insurers predict risk more accurately, improve profitability, and automate underwriting and claims. Our SaaS platform taps into one of the industry's largest data lakes—tens of millions of policies and claims—to deliver deep, actionable insights.
Requirements
- Strong technical background; ability to work closely with engineers on architecture, data pipelines, and APIs.
- Experience working with data platforms and ML/analytics products.
- Familiarity with tools like Jira, Confluence, GitHub, and data documentation platforms.
- Experience with cloud data environments (AWS, Snowflake, Databricks, or similar) and modern CI/CD or data ops practices.
- Knowledge of data orchestration tools like (Airflow, Dagster, Prefect, etc.) a plus.
- Experience with SQL and Python a plus.
- Exposure to ML/AI productization, risk scoring models, or predictive analytics platforms.
Responsibilities
- Translate product strategy and roadmap themes into detailed epics, user stories, and delivery plans.
- Manage sprint ceremonies, backlog refinement, and cross-team coordination.
- Identify and mitigate technical risks and delivery blockers.
- Track progress across engineering and data teams, ensuring alignment with strategic goals.
- Support product launches, feature rollouts, and customer migrations—managing timelines, QA, and stakeholder communications.
- Own documentation for technical product specs, infrastructure updates, data integrations, and model deployment workflows.
- Partner with data engineering, data science, software, and clinical informatics teams to ensure reliable ingestion, transformation, and integration of data between systems
Other
- 5+ years of experience in technical product ownership, program management, or engineering project management, preferably within healthcare data/analytics platforms.
- Strong understanding of agile methodologies and product development lifecycle.
- Proven success leading and managing cross-team dependencies across product, engineering, and data science.
- Experience in health analytics, underwriting, or population health products a plus.
- Prior work in regulated health data environments (HIPAA, de-identification, SOC 2) a plus.
