Data Science Product Manager

Liberty Mutual Insurance • Boston, MA

1d • $163,500 - $201,501

About The Position

The Data Science Product Manager leads the strategy, development, deployment, and scaling of AI-powered intelligence products that improve underwriting decision-making and business outcomes. The role spans underwriting, product management, data science, and technology, and focuses on expanding successful products across North American underwriting segments, profit centers, and lines of business to maximize reuse, consistency, adoption, and enterprise impact.

The position partners with business leaders, data scientists, engineers, architects, and technology teams to identify high-value opportunities, set product strategy and roadmaps, prioritize investments, and deliver scalable solutions with measurable business value. Product capabilities may include machine learning, predictive modeling, NLP, LLM-powered applications, RAG architectures, agentic workflows, recommendation engines, and decision intelligence.

The role also oversees product valuation and outcome measurement, supports production deployment and MLOps, and ensures appropriate monitoring, reliability, auditability, explainability, governance, and responsible AI practices. It leads stakeholder alignment, organizational readiness, training, adoption, and change management. The description also states that the position is responsible for the management, technical oversight, and development of a team of data science and advanced analytics staff, and collaborates with business partners on tools and services such as modeling, dashboards, and decision aids.

Requirements

• Bachelor’s or Master’s degree in a quantitative field.
• At least 5 years of experience in insurance, actuarial work, data science, analytics, technology, or advanced analytics product management.
• Strong understanding of data science, machine learning, Generative AI, and agentic AI concepts and methodologies.
• Knowledge of data science product development, deployment, MLOps, monitoring, and governance practices.
• Understanding of technology platforms, architectures, APIs, and tools that support AI/ML solutions.
• Experience translating business needs into product strategy and prioritizing work according to customer and business value.
• Strong communication, relationship-building, and stakeholder management skills, including the ability to influence senior leaders and align cross-functional teams.
• Ability to communicate technical concepts and analytical findings to technical and non-technical audiences.
• Proficiency with analytical and advanced analytics product management tools, including PowerPoint, SQL, Python, and modern data and AI technologies.

Nice To Haves

• Prior advanced analytics product management experience supporting data science, machine learning, AI, or intelligence solutions.
• Understanding of insurance underwriting practices, risk assessment, pricing, and decision-support workflows.
• Experience building, deploying, or scaling Generative AI, agentic, or advanced analytics solutions.
• Strong foundation in data science, machine learning, and AI technologies.
• Strong advanced analytics product management experience supporting complex intelligence products.
• Proven experience scaling successful solutions across multiple business segments, underwriting organizations, or product lines.
• Experience with MLOps, AI governance, model monitoring, and enterprise deployment practices.
• Experience building Generative AI solutions, RAG applications, and agentic systems.
• Familiarity with Generative AI evaluation methodologies and Responsible AI principles.
• Track record of cross-functional collaboration and leadership in a large enterprise environment.
• Experience driving adoption and value realization for AI-enabled products at scale.
• ACAS or equivalent insurance industry credentials.

Responsibilities

• Define and drive strategy, multi-year roadmaps, and investment priorities for a portfolio of enterprise intelligence products.
• Identify opportunities where AI, machine learning, Generative AI, and decision intelligence can create measurable business impact.
• Establish product vision, success metrics, and adoption strategies aligned with organizational and enterprise objectives.
• Develop business cases, financial valuations, and outcome-based prioritization to guide product portfolio decisions.
• Partner with data scientists, engineers, architects, and technology teams throughout the product lifecycle to deliver scalable AI and machine learning solutions.
• Lead development and deployment of predictive modeling, NLP, LLM-powered applications, RAG architectures, agentic workflows, recommendation engines, and decision intelligence solutions.
• Collaborate on production deployment strategies, MLOps, observability, model monitoring, reliability, and governance.
• Influence technical direction for AI platforms, reusable services, model orchestration, and enterprise intelligence architecture.
• Represent customer needs and translate business challenges into scalable product solutions.
• Build alignment among senior stakeholders and manage competing priorities, trade-offs, dependencies, and resource constraints.
• Communicate technical concepts, AI capabilities, and product outcomes to technical and non-technical audiences, including executive leadership.
• Drive organizational readiness, training, adoption, and change management to maximize business value.
• Own product valuation frameworks, prioritization methodologies, and outcome measurement processes.
• Define, measure, and communicate KPIs for business outcomes, adoption, operational efficiency, profitability, and product performance.
• Lead product planning, including roadmap development, PI planning, dependency management, and release coordination.
• Guide prioritization of technical debt, platform modernization, and product scalability initiatives.
• Ensure products comply with Responsible AI principles, governance standards, model risk management practices, and regulatory requirements.
• Partner with governance and technology teams to implement monitoring, auditability, explainability, and feedback mechanisms.
• Establish frameworks to evaluate, measure, and continuously improve model and Generative AI performance.
• Manage, provide technical oversight for, and develop a team of data science and advanced analytics staff.
• Collaborate with business partners to develop modeling, dashboard, and decision-aid tools and services.
• Present findings, share insights, and make recommendations affecting profitability, growth, and/or customer satisfaction.
• Provide tactical input on highly technically complex projects that drive change across a function, SBU, or corporate department.
• Supervise and perform complex predictive analytics projects involving technical and creative work.
• Engage with the data science community and lead cross-functional working groups.

Benefits

• Comprehensive benefits that support employees’ lives and well-being.
• Workplace flexibility.
• Professional development opportunities.
• Employee Resource Groups.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior