Senior Technical Product Manager

Responsibilities

• Define and own the product vision, strategy, and roadmap for data science and machine learning initiatives
• Translate business problems into well-scoped data science use cases and product requirements
• Partner closely with data scientists, data engineers, and ML engineers to deliver models and data products end to end
• Prioritize features, experiments, and model improvements based on impact, feasibility, and risk
• Lead discovery efforts to identify opportunities where data science can create meaningful business value
• Ensure models are production-ready, monitored, and continuously improved post-launch
• Collaborate with engineering and platform teams on data infrastructure, tooling, and scalability
• Communicate clearly with stakeholders on trade-offs, timelines, model performance, and outcomes
• Define success metrics and evaluate product performance using quantitative and qualitative insights
• Ensure responsible use of data, including privacy, fairness, and ethical considerations

• 3+ years experience in a consumer-facing product management or equivalent role
• Experience working closely with engineers and designers in agile or scrum environments
• Strong analytical, problem-solving, and organizational skills
• Excellent written and verbal communication skills, including the ability to translate complex ideas for different audiences
• Customer-first mindset with a passion for creating intuitive, impactful products
• Proficiency in product tools like Jira, Confluence, Figma, and data tools like Looker, Amplitude, or similar
• Experience in insurance/insurtech is a plus; experience operating in a regulated industry (e.g., financial services, healthcare) is also strongly preferred

Demonstrates expertise in defining product vision and strategy for data science and machine learning initiatives, with a strong focus on translating business problems into actionable data science use cases. Proficient in collaborating with cross-functional teams to ensure models are production-ready and continuously improved while maintaining ethical data practices.