Role Description The Lead Product Manager, Data Transformation & Modeling is responsible for leading the design, optimization, and continuous evolution of insurance products through advanced data analytics, actuarial modeling, and modern data infrastructure. This role sits at the intersection of product strategy, underwriting, actuarial science, and data science, translating complex analytical insights into actionable product decisions that drive profitability, growth, and competitive differentiation. This individual will own product outcomes for clients across pricing, segmentation, underwriting rules, and performance monitoring, with a strong emphasis on transforming raw data into scalable, model-driven product capabilities. Key Responsibilities - Own the end-to-end product lifecycle for assigned insurance products (e.g., BOP, GL, Property, Auto, Homeowners), from concept through launch and ongoing optimization. - Define product vision and roadmap grounded in data-driven insights, market benchmarks, and portfolio performance. - Translate business objectives (growth, profitability, retention) into analytical and modeling requirements. - Lead initiatives to modernize product data pipelines, ensuring clean, well-structured, and analysis-ready datasets across policy, quote, exposure, loss, and third-party data sources. - Ensure alignment between source systems, analytical datasets, and downstream product tools (pricing engines, underwriting workbenches, dashboards). - Collaborate closely to design, implement, and refine pricing models, segmentation frameworks, and underwriting scorecards. - Translate model outputs (relativities, lift, elasticities, loss drivers) into clear product rules, rating variables, and underwriting guidance. - Evaluate and integrate third-party data sources to enhance risk differentiation and pricing accuracy. - Support model monitoring, performance diagnostics, and recalibration strategies over time. - Ensure regulatory, filing, and compliance considerations are incorporated into product and modeling decisions. - Lead post-launch reviews and ongoing optimization efforts using empirical performance data. - Identify opportunities for automation, simplification, and straight-through processing enabled by data and models. Qualifications - 5+ years of experience in insurance product management, actuarial, analytics, or a closely related role. - Strong understanding of P&C insurance products, rating plans, underwriting workflows, and regulatory considerations. - Experience with modern analytics tools and platforms (e.g., SQL, Python, R, Tableau/Power BI, Akur8, Earnix, Radar, or similar). - Familiarity with third-party insurance data sources (credit, property, telematics, geospatial, business data). - Exposure to pricing modernization, underwriting automation, or digital transformation initiatives. - Hands-on experience working with analytical outputs (e.g., GLMs, machine learning models, segmentation analyses). - Proven ability to translate technical and actuarial concepts into clear product and business decisions. - Experience working with large, complex datasets and collaborating with data engineering or data science teams. - Strong executive communication skills and comfort presenting analytical insights to senior leadership.

• Write clear, well-defined requirements, user stories, and flows that enable the engineering team to execute them efficiently and effectively. • Work closely with engineering teams to define, prioritize, and deliver impactful features. • Collaborate with internal and external stakeholders to refine product-market fit and enhance user experience. • Analyze customer feedback, usage data, and industry trends to drive continuous improvement. • Define success metrics, track adoption, and iterate based on performance insights. • Partner with marketing and sales to craft compelling go-to-market strategies. • Act as the customer’s advocate internally, working with cross-functional teams to drive successful implementation, adoption, growth, and renewals. • Influence and contribute to PerfectScale’s integration within DoiT’s ecosystem.

• Write clear, well-defined requirements, user stories, and flows that enable the engineering team to execute them efficiently and effectively. • Work closely with engineering teams to define, prioritize, and deliver impactful features. • Collaborate with internal and external stakeholders to refine product-market fit and enhance user experience. • Analyze customer feedback, usage data, and industry trends to drive continuous improvement. • Define success metrics, track adoption, and iterate based on performance insights. • Partner with marketing and sales to craft compelling go-to-market strategies. • Act as the customer’s advocate internally, working with cross-functional teams to drive successful implementation, adoption, growth, and renewals. • Influence and contribute to PerfectScale’s integration within DoiT’s ecosystem.

• Translate the VP’s strategy into a sequenced backlog of problems, user stories, and acceptance criteria that the Engineering team can deliver continuously. • Keep work “thin-sliced” so the team can ship frequently, learn quickly, and reduce risk through iteration. • Maintain a disciplined cadence of backlog refinement, sprint readiness, and scope negotiation to keep delivery predictable without slowing innovation. • Leverage your hands-on DevOps experience and expertise in the real-world workflows of the professionals you’re building for - especially cloud operators and practitioners. • Use firsthand domain knowledge and ongoing customer conversations to identify friction, gaps, and opportunities for differentiation. • Prioritize enhancements that materially improve outcomes: speed, reliability, clarity, safety, governance, cost impact, or operational simplicity. • Often speak with customers (aiming for 3-5 calls a week) and cross-functional DoiT stakeholders regularly to test ideas, validate assumptions, and pressure-test tradeoffs. • Communicate clearly and confidently: share concepts early, take feedback openly, synthesize input into decisions, and explain “why” in a way that builds trust. • Rely on your hands-on, DevOps experience to go deep with stakeholders (internal/external) into the technical details of the problem, our solution, and the contexts and environments in which both of these exist. • Support beta/design-partner motions where appropriate by shaping hypotheses, success criteria, and rollout plans. • Operate as a core member of the component’s Engineering team - present in daily collaboration, crisp in decision-making, and pragmatic about constraints. • Partner with Engineering leads on sequencing, technical tradeoffs, and delivery planning - ensuring product intent survives implementation realities. • Ensure the component is cohesive across UX, APIs, data, and operational behavior - and that it meets a high bar for reliability and customer trust. • Define what “success” means for the component with clear metrics (adoption, activation, workflow completion, time-to-value, retention, reliability signals, customer-reported impact). • Instrument learning: make sure releases are measurable and that insights reliably flow back into prioritization. • Own release readiness for your component: documentation, enablement inputs, and clear communication of what’s new and why it matters. • Ensure product requirements account for real-world cloud environments: IAM boundaries, auditability, guardrails, and safe defaults. • Partner with security, support, and customer teams to anticipate edge cases and ensure the component earns trust over time.