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
Requirements
- 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
Core Competencies
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.
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