Product Delivery Lead - Insurance Data

Technology Organization Overview

KKR's Technology team is responsible for building and supporting the firm's technological foundation including a globally distributed infrastructure, information security, and the application and data platforms. The team drives a culture of technology excellence across the firm through efficient workflow automation, democratization of data through modern data and collaboration platforms, and more recently through research and development of Generative AI based tools and services. Technology is regarded as a key business enabler at KKR and is an important accelerator to drive towards global scale creation and business process transformation. The Technology team consists of highly technical and business-centric technologists with the ability to form strong partnerships across all of our businesses.

Team Overview

Our dedicated Product & Delivery function drives execution discipline across multiple technology teams with a goal to consistently deliver excellence serving our business needs. The Product & Delivery leads are responsible for defining the technology strategy, understanding the required capabilities, and delivery on the technology priorities for the business areas and functions they are partnered with.

Position Summary

We are seeking a Tech Product & Delivery Lead for Insurance Data and AI to own the end-to-end strategy, roadmap, and delivery of technology solutions that power KKR's insurance data and AI capabilities – from policy, investments, and actuarial data platform architecture, data governance and quality, and the enterprise insurance data catalog through machine learning infrastructure, generative AI tooling, and firmwide insurance analytics enablement. Partnering closely with the Data and AI leadership team – as well as our Insurance Data Engineering and AI/ML Engineering teams – this leader will shape the multi-year vision for Insurance Data and AI technology, build and lead a team of product managers and delivery leads, and personally drive the most complex, highest-impact programs across the portfolio. The role acts as the senior bridge between Insurance Data and AI stakeholders and engineering, ensuring deep understanding of business priorities to drive the right prioritization, modernization, and responsible-AI-enabled transformation of how KKR's insurance platform companies collect, govern, and derive value from policy, investments, actuarial, and distribution data globally.

Key Responsibilities

Technology Strategy & Roadmap

• Define and own the multi-year technology strategy and roadmap for the end-to-end Insurance Data and AI technology stack – spanning policy, investments, and actuarial data platform and lakehouse architecture, data governance and quality, master and reference data management, machine learning infrastructure, and generative AI tooling – ensuring alignment with the firm's insurance data strategy and business priorities
• Drive prioritization and governance of the Insurance Data and AI technology portfolio, balancing near-term operational needs (data quality remediation, model retraining cycles, and platform capacity reviews) with long-term platform modernization aligned to the Insurance Data and AI leadership team's vision
• Stay current on emerging trends across insurance data platforms, MLOps, LLM / generative AI tooling, and actuarial and investments data governance technology – bringing innovation into the roadmap and shaping the perspective of Insurance Data and AI leadership on where to invest

Stakeholder Partnership & Requirements Management

• Partner with the Data Operations, Actuarial,, Underwriting Analytics, Model Risk Management, and business-unit data stewards teams to understand business requirements and translate complex insurance data and AI needs into practical, scalable technology solutions
• Collaborate closely with engineering leads to design scalable, secure, and efficient solutions supporting the full insurance data-to-insight lifecycle (e.g., policy, investments, and actuarial data ingestion and integration, data quality and governance, feature engineering, model development and deployment, and consumption via risk, investments, and actuarial analytics applications)
• Serve as the senior technology partner to the Insurance Data and AI function, facilitating cross-functional collaboration across Data Operations, Actuarial, Investments,, Underwriting Analytics, Legal, Compliance, and Technology, and building deep partnerships with insurance business leaders globally

Technology Lifecycle & Delivery

• Lead a team of product managers and delivery leads, setting standards for discovery, requirements management, delivery cadence, and adoption across the Insurance Data and AI technology portfolio
• Oversee end-to-end product lifecycle for all Insurance Data and AI platforms – including requirements gathering, vendor selection, solution design, configuration, testing, rollout, change management, adoption, and continuous improvement
• Drive the development and implementation of platforms supporting the full insurance data-to-insight lifecycle, including data lakehouse and warehouse platforms, data catalog and governance tools, ETL / ELT and orchestration for policy / investments / actuarial data, MLOps and model lifecycle management, generative AI / LLM tooling, and self-service BI and analytics platforms
• Ensure Insurance Data and AI technology solutions deliver an integrated, modern data and analytics experience, with workflow automation, self-service access, and real-time insights into data quality, model performance, and platform adoption metrics
• Own delivery accountability for the portfolio – including budget, vendor relationships, SOWs, milestones, risks, and stakeholder communication – partnering with the Program Management function to ensure execution discipline across concurrent workstreams

Technology Innovation & Transformation

• Champion the modernization and automation of the Insurance Data and AI technology stack, including cloud-native architecture, a consolidated insurance data platform, modern integration patterns, and the application of advanced analytics and generative AI to underwriting, investments, and actuarial data quality, model deployment, and firmwide productivity use cases
• Evaluate and integrate best-of-breed Insurance Data and AI technology solutions (e.g., data lakehouse, data catalog / governance, MLOps, and generative AI platforms) as needed to enhance capabilities
• Lead the responsible adoption of AI-enabled capabilities across the insurance data lifecycle – for example, in actuarial model performance monitoring and drift detection, and generative AI copilots – with appropriate governance and human oversight
• Drive adoption of new solutions and embed them into the day-to-day workflows of users, ensuring measurable impact and a best-in-class user experience

Governance, Compliance & Risk Management

• Ensure all Insurance Data and AI technology platforms meet global regulatory expectations, including data privacy (e.g., GDPR, CCPA), model risk management standards, NAIC and state insurance regulatory requirements on data and AI use, and emerging AI governance regulations, and internal governance standards specific to policy, investments, and actuarial data lineage, quality, and responsible AI
• Partner with Legal, Compliance, Information Security, and Model Risk Management to align solutions with enterprise risk management frameworks and to operationalize controls across the insurance data and model lifecycle
• Implement controls, access models, and risk mitigation protocols aligned with enterprise technology policies, with particular attention to data access governance, model explainability, and confidentiality of policyholder, investments, and underwriting data

Performance Monitoring & Continuous Improvement

• Monitor adoption and performance of deployed Insurance Data and AI platforms, leveraging quantitative and qualitative feedback to inform continuous enhancements
• Lead continuous improvement initiatives to enhance system performance, user experience, process efficiency, and business value delivery across the Insurance Data and AI technology portfolio
• Define, measure, and report on key product and delivery metrics – including adoption, cycle time, NPS, data quality scores, model deployment velocity, and ROI – to Insurance Data and AI leadership and the Technology leadership team

Qualifications

Experience:

• 12+ years of experience in system implementations, product management, technology delivery, or transformation roles, with meaningful time spent leading Insurance Data and AI technology programs, ideally within life insurance, reinsurance, or large insurance enterprise environments
• Proven track record of building and leading product and delivery teams, and of personally delivering enterprise insurance data and AI technology programs at scale across multiple geographies
• Experience partnering with engineering teams, vendors, and systems integrators, and leading complex, cross-functional initiatives in a matrixed environment with executive stakeholders

Domain Expertise:

• Deep understanding of the end-to-end insurance data-to-insight lifecycle and the operating model of a modern insurance data and AI function – including policy, investments, and actuarial data platform architecture, data governance and quality, MLOps, generative AI enablement, and analytics / BI
• Hands-on familiarity with leading Insurance Data and AI technology platforms across the stack – for example, Snowflake, Databricks, or Microsoft Fabric for data platform / lakehouse; Collibra or Alation for data catalog / governance; MLflow, SageMaker, or Vertex AI for MLOps; Azure OpenAI or Bedrock for generative AI; and Power BI, Tableau, or Sigma for analytics / BI]
• Knowledge of data privacy regulation (e.g., GDPR, CCPA), model risk management, NAIC / state insurance regulatory expectations on data and AI, and emerging AI governance frameworks and their implications on insurance data and AI technology and architecture

Technical Knowledge:

• Technical fluency with enterprise insurance data and AI/ML platforms, identity and access management, data governance, workflow automation, and emerging AI/ML tools applied to analytics use cases
• Experience designing and implementing workflow automation platforms, integration architectures (APIs, event streams, iPaaS), and insurance-specific data models and semantic layers across multiple best-of-breed vendor systems
• Familiarity with cloud architecture, modern data management, and analytics tools (e.g., Python, SQL, Power BI, Sigma, Tableau, or equivalent) and with building a governed data and analytics layer on top of operational underwriting, investments, and actuarial systems
• Understanding of enterprise DevOps, CI/CD practices, and modern SDLC frameworks, and how they apply in a configuration-heavy SaaS insurance data / AI platform environment

Competencies & Skills

Strategic Mindset:

• Ability to design and communicate a long-term vision for the Insurance Data and AI technology stack while delivering incremental business value each cycle
• Strategic thinking with the ability to align product and delivery initiatives with broader enterprise technology vision, the firm's insurance data strategy, and business objectives

Stakeholder Management:

• Strong ability to engage, influence, and align diverse stakeholders across Data Governance, Actuarial, Legal, Compliance, and Technology functions globally
• Excellent articulation and executive presence to engage Data Ops, Insurance Data and AI leadership, and Technology leadership, as well as to coach and mentor product and delivery teams
• Ability to build consensus and drive execution in a complex, fast-moving, and highly confidential environment

Execution Discipline:

• Skilled at prioritization, governance, and managing delivery timelines across a multi-workstream portfolio without compromising quality
• Strong program management capabilities to drive execution discipline across multiple technology teams, vendors, and concurrent insurance data and AI delivery cycles (e.g., data quality remediation sprints, model retraining cycles, platform capacity reviews)

Analytical & Problem-Solving:

• Strong skills in problem-solving, data analysis, and translating insights into product and delivery decisions
• Ability to balance technical feasibility, vendor capabilities, business needs, and end-user experience

Change Leadership:

• Able to drive adoption of new solutions and embed them into business and insurance data processes, including the change management required for global Insurance Data and AI rollouts
• Passion for innovation and for driving transformational change in how the firm's insurance platform companies use data and AI – with empathy for the user experience

Communication:

• Excellent written and verbal communication skills with the ability to translate complex technical concepts for non-technical audiences, including senior insurance data and business leaders
• Strong collaboration skills to work effectively across global, cross-functional teams and to lead distributed product and delivery teams

Education

Bachelor's degree in Computer Science, Data Science, Statistics, or Actuarial Science; MBA or advanced degree preferred