Data Product Owner

MSIG USA•Warren Township, NJ

1d•Hybrid

About The Position

This role leads the data strategy, roadmap, and execution across Product, Underwriting, and Actuarial domains. You will define how data is captured, governed, modeled, and delivered across the insurance value chain so business leaders can make faster, more accurate, and more confident risk and pricing decisions. You will own the development of trusted data products spanning policy structure, rate filings, exposure, pricing, loss costs, risk selection, reserving, IBNR, schedule P, and actuarial indications. These data products will support operational efficiency, pricing adequacy, regulatory compliance, portfolio steering, reserve development monitoring, risk segmentation, and product profitability analysis. You will work closely with Product, Underwriting, Actuarial, Claims, Finance, Reinsurance, IT, and Data Engineering to ensure consistent, high-quality data from submission and quote through policy issuance, renewal, and ultimate loss development.

Requirements

5+ years of experience in data product ownership, analytics, or domain-focused data roles; P&C insurance experience strongly preferred.
Strong understanding of underwriting operations, actuarial concepts (IBNR, loss development, Schedule P, loss costs), and insurance product management.
Proven experience partnering with data engineering teams on pipelines, data models, and quality frameworks.
Ability to translate complex insurance and actuarial concepts into clear technical requirements.
Strong facilitation, communication, and stakeholder alignment skills.
Hands-on experience with the Azure analytics ecosystem: Azure Data Platform, Microsoft Fabric, Azure Data Factory, Databricks, Azure AI Studio / Azure OpenAI, Power BI.

Nice To Haves

Experience working directly in Underwriting, Product Management, or Actuarial teams within a P&C insurer.
Familiarity with policy administration and rating systems (Guidewire PolicyCenter, Duck Creek, Applied Epic, or similar).
Experience with submission and exposure data, SOVs, loss runs, and structured/unstructured underwriting documents.
Knowledge of actuarial methods including chain ladder, Bornhuetter-Ferguson, and Cape Cod techniques.
Familiarity with state DOI regulatory requirements, rate/form filing processes, and NAIC reporting standards.
Azure, Power BI, or data platform certifications.

Responsibilities

Define and own the end-to-end data strategy across Product, Underwriting, and Actuarial domains.
Establish data standards, ownership, quality controls, lineage, and governance for internal and third-party datasets including ISO, NCCI, bureau data, and vendor risk scores.
Maintain a unified data dictionary and harmonized definitions across product, underwriting, actuarial, claims, and finance teams.
Ensure data workflows meet audit, regulatory, rate filing, and compliance requirements including state DOI and NAIC obligations.
Partner with Legal and Compliance to support rate filing readiness, regulatory reporting, and actuarial certification requirements.
Partner with data engineering to design and improve pipelines feeding policy administration systems, rating engines, actuarial reserving models, analytics, and AI use cases.
Validate data models and transformations in Azure Data Platform, Microsoft Fabric, and Databricks.
Define and enforce data quality rules for common issues such as missing exposure fields, submission-to-bind data gaps, rating factor inconsistencies, endorsement mismatches, and development triangle discrepancies.
Set expectations for data availability, reliability SLAs, and lineage tracking across policy and actuarial data products.
Own the roadmap for dashboards, KPIs, and reporting across underwriting performance, product profitability, and actuarial reserve monitoring.
Enable self-service analytics through Power BI and Fabric semantic models, reducing reliance on manual spreadsheet-based actuarial and underwriting reporting.
Standardize metrics used by Product, Underwriting, Actuarial, Claims, Finance, and Reinsurance teams — including written premium, earned premium, loss ratio, combined ratio, hit ratio, renewal retention, and rate adequacy.
Support operational decisions including risk selection, pricing adjustments, portfolio rebalancing, treaty structuring, and reserve releases.
Identify and prioritize AI and advanced analytics use cases, including: Predictive risk scoring and underwriting triage models, Price elasticity and appetite modeling, Automated extraction from submissions, SOVs, loss runs, and policy documents, Reserve development and IBNR estimation using machine learning, Exposure accumulation and catastrophe risk analytics, Loss cost trend analysis and actuarial indication automation, Generative AI summaries for underwriting files, actuarial memos, and product performance narratives.
Partner with Data Science and Azure AI Studio teams to ensure models are explainable, validated, and embedded into underwriting and actuarial workflows.
Own and prioritize the data product backlog across Product, Underwriting, and Actuarial domains.
Translate business needs into clear user stories with acceptance criteria tied to measurable value — such as improved rate adequacy, faster actuarial close, or increased straight-through processing rates.
Lead Agile ceremonies, roadmap reviews, and stakeholder demos.
Facilitate alignment across Product, Underwriting, Actuarial, Claims, Finance, Reinsurance, and IT.
Improve underwriting outcomes and risk selection through better submission data, risk scoring, and appetite visibility.
Accelerate actuarial close cycles and reduce manual data preparation in reserving and pricing workflows.
Strengthen product decisions with clear visibility into loss costs, rate adequacy, exposure trends, and competitive positioning.
Enhance feedback loops from claims and finance to underwriting and product teams with actionable loss and portfolio insights.
Improve regulatory and rate filing readiness through strong governance, data documentation, and actuarial data auditability.