• Optimize internal workflows for return initiation, RMA creation, inventory disposition, and refund processing. • Manage integrations between Narvar, Shopify, OMS, NetSuite, and 3PL partners to support automated returns. • Ensure accurate, real-time flows of data for RMAs, refunds, inventory adjustments, and financial reconciliation. • Collaborate with Operations, Logistics, and 3PLs to design scalable return processing, routing, and restocking workflows. • Translate complex return requirements into technical specifications, user stories, and data flow diagrams. • Partner with engineering to scope, prioritize, and deliver return system enhancements using Agile practices.

• Demonstrates excelling business acumen, knows latest industry trends and is looked at as an internal expert on the product as well as the associated competitive landscape. • Interacts with customers/stakeholders in VOC requirements-gathering, fine tuning, and obtaining product feedback. • Works with cross-functional teams to deliver features and significant products. • Possesses a deep understanding of the technology stack and impact on final product. • Routinely collaborates with UX in design decisions. Conducts customer and stakeholder interviews and elaborates on personas. • Demonstrate superior persuasion and influencing skills that ensure alignment between customer, product manager and engineering teams. • Owns the releases and sprint backlogs short-term roadmap for MVPs and quarterly releases. • Owns and manages the backlog; continuously orders and prioritizes to ensure that 1-2 sprints/iterations of backlog are always ready. • Prioritizes continuously in accordance with the understanding and validation of customer problems and needs. • Demonstrates strategic level skills in problem decomposition and ability to navigate through ambiguity. • Engages frequently (50% of the time) with the development team; facilitates discussions, provides clarification, story acceptance and refinement, testing and validation; contributes to design activities and decisions. • Translates unstructured or ambiguous work requests into actionable user stories and work unit. • Partners with Development Leadership/Service Owners to ensure healthy development process. • Mentors junior team members. • Expert in Agile Methodology.

• Operating in lock-step with product management to create strategic direction for product needs • Delivering a clear and descriptive set of requirements to enable product features and functionality • Interacting with customers/stakeholders for VOC requirements-gathering, fine-tuning, and feedback • Working with cross-functional teams to deliver features and significant products • Collaborating with UX in design decisions • Conducting customer and stakeholder interviews and elaborating on personas • Owning and managing the backlog; ordering and prioritizing to ensure readiness for sprints • Engaging frequently with the development team; facilitating discussions and providing clarifications • Translating unstructured work requests into actionable user stories

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.