Data Product Manager

We need to output HTML fragment with sections: Use

for section headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Must include only sections that have real specific content. Must not repeat job title as heading. Must not include Job ID etc. Must preserve all substantive info, no boilerplate. Remove cookie/navigation/legal/EEO etc. Must not include duplicated lines. Must not include application instructions. Must not include filler like "commensurate with experience". Must not create Pay/Schedule/Benefits sections unless posting actually states them. The posting includes "Hiscox USA Offers" with benefits list, salary range, etc. So we have Benefits and Pay sections. Also schedule: mentions hybrid and requires two days in office weekly. So schedule info present. So we can include Schedule section. We need sections: About the role (the "About The Role" paragraph). Responsibilities (the "What You'll Do" bullet list). Requirements (the "Our Must Haves" list). Qualifications maybe combine? The instruction says use headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We can include those that have content. We have About the role, Responsibilities, Requirements (must haves), Qualifications (maybe nice to haves?), Skills (maybe not separate). Could combine nice to haves under Qualifications or Skills. But we need to avoid filler. The nice-to-haves are specific, so we could put them under Qualifications. Skills maybe not needed if not explicit. Could omit Skills section if no explicit skills list beyond must haves. But we have must haves includes deep analytical skills, SQL, etc. Could treat that as Skills section. However instruction: prefer a few substantive sections over many thin ones. We could combine Must Haves and Nice To Haves into Requirements and Qualifications. Let's decide: - About the role: paragraph(s) from "About The Role" and maybe "The Team" description? The Team is also substantive but not a heading; we could incorporate into About the role or maybe a separate section? The instruction only lists certain headings; we could add other headings if needed? It says use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid other headings, but suggests those as examples. Safer to stick to those. We can put team description under About the role. - Responsibilities: from "What You'll Do" bullet points. - Requirements: from "Our Must Haves". - Qualifications: from "Our Nice To Haves". - Benefits: from "Hiscox USA Offers" list (excluding salary? maybe salary goes to Pay). Benefits include health insurance, 401k, PTO, etc. - Pay: salary range "$100-$130k" and note about actual salary determination. - Schedule: hybrid, two days in office weekly. We must not include boilerplate like "Hiscox Values", "At Hiscox our spirit..." etc. Also remove "Please note that this position is hybrid and requires two (2) days in our office weekly**" - we keep schedule info but remove the note markers. Also remove "Hiscox Values" section. Remove "About Hiscox USA" description (maybe not needed as it's boilerplate? It's substantive about company but could be considered background; but instruction says preserve ALL substantive information; do not invent or summarize away detail. The "About Hiscox USA" is substantive about the company; but is it needed? It's not about the role directly but could be considered company overview. The instruction didn't forbid including it, but we need to decide if we should include. It's not under any of the headings; we could add a section like "About the Company" but not in list. Might be considered extra but not prohibited. However they said prefer a few substantive sections over many thin ones. Including company overview adds another section. Might be okay but could be considered boilerplate? It's specific to Hiscox USA. I'd include it as a section maybe "About the Company". But the instruction only gave examples; not strict. Safer to omit to keep focused on role. However they said preserve ALL substantive information; removing company overview would lose substantive info. But maybe it's considered boilerplate? It's describing the business; could be considered relevant. I'd include a short paragraph under maybe "About the Company" but not in the prescribed headings. Could we incorporate into About the role? It's not about the role but could be added as a paragraph after About the role before Responsibilities. That would preserve info without extra heading. Let's do that: after About the role paragraph, include a paragraph about Hiscox USA (the description). That keeps substantive info. Now we need to format lists with

• . For responsibilities, we have many bullet points; we need to capture each as
• . Must preserve all detail; not summarize. We'll need to break the long paragraphs into list items. The "What You'll Do" section includes headings like "Product Ownership, Vision & Strategy:" then a paragraph, then "Horizon Scanning & Innovation Drive", then "Stakeholder & Delivery Partnership". We could treat each bold heading as a list item containing the paragraph. Or we could break into multiple list items for each bullet. Simpler: treat each bold heading as a list item with its content. Similarly for Requirements (Must Haves) each line is a bullet; we can list each. Nice To Haves similarly. Benefits: list each benefit. Pay: maybe a paragraph with salary range and note. Schedule: a paragraph. Now produce HTML fragment. We must not include or . Just the fragment. Let's craft. First, About the role: combine Team description and About The Role paragraph. Team paragraph: "The Team Our Data organization creates and manages a portfolio of trusted, reusable, and governed data products that deliver measurable business value and drive profitable growth for the company. We operate an agile, product-based delivery model with cross-functional scrum teams aligned to business objectives and key results. We are in the midst of a deliberate shift toward an AI-first operating model — embedding AI, GenAI, and intelligent automation into how we discover, build, deploy, and scale data products, and this role sits at the leading edge of that shift, partnering with our data science, AI engineering, and intelligent automation teams to turn emerging capability into real business outcomes." About The Role paragraph: "We are looking for a Product Manager to own the data products and capabilities delivered by our intelligent automation, data science, and AI engineering teams. Where our core and steady-state data products keep the business running, this role is focused on what's next — defining, prioritizing, and delivering the models, automations, and AI-enabled capabilities that create new value for business stakeholders across the US value chain (marketing, operations, underwriting, claims, distribution, and finance). This is a forward-looking, technically fluent product role for someone who understands both the art of the possible and the discipline of shipping production-grade data science, automation, and AI products. You will partner daily with data scientists, ML/AI engineers, and automation engineers to translate ambiguous business problems into well-scoped product initiatives, and you will be expected to actively drive horizon scanning — working with these teams, business stakeholders, vendors, and the broader industry to proactively anticipate, evaluate, and bring forward what's next in data science, automation, and AI so the organization stays ahead rather than reactive. You will own the roadmap, backlog, and outcomes for this product area end to end, ensuring every automation, model, and AI capability we build is grounded in a clear business case, responsibly governed, and designed to scale." We can combine into one

  maybe two paragraphs. Now Responsibilities: list items. We need to capture each bullet under the headings. Let's extract: Product Ownership, Vision & Strategy: - Define and own the vision, strategy, and roadmap for data products and capabilities delivered by the intelligent automation, data science, and AI engineering teams. - Translate business problems and opportunities into well-defined product initiatives, with clear hypotheses, success metrics, and business cases for automation, data science, and AI investments. - Prioritize and sequence a backlog that balances quick-win automations, applied data science use cases, and larger AI/ML product builds against business value and delivery capacity. - Champion the organization's AI-first operating model, ensuring new data products are designed from the outset for responsible AI use, scalability, and integration with the broader data platform. Horizon Scanning & Innovation Drive: - Structured horizon scanning with the data science, automation, and AI engineering teams, business stakeholders, industry peers, and vendors to proactively identify emerging tools, techniques, and use cases. - Evaluate emerging AI, GenAI, machine learning, and automation capabilities for applicability and value to the business, and translate promising innovations into proof-of-value and pilot proposals. - Maintain an active view of the competitive and industry landscape in P&C insurance data science and automation, sharing relevant insights with leadership and stakeholders to inform strategy. - Foster a culture of experimentation, running structured pilots and proofs of concept to test new capabilities before committing to full-scale investment. - Perform hands-on data analysis and SQL-based investigation to validate data quality, troubleshoot issues, and inform product decisions. - Manage capability-related incidents, questions, and escalations, communicating clearly with technical and non-technical audiences alike. - Validate and sign off on innovation products, confirming accuracy, functionality, and Enterprise Data management (EDM) standards are met. Stakeholder & Delivery Partnership: - Partner with business stakeholders across US business functions to understand their goals and identify opportunities for automation, data science, and AI to create value. - Work closely with data scientists, ML/AI engineers, and automation developers to scope, refine, and validate requirements, acceptance criteria, and model/automation performance expectations. - Partner with the Delivery Manager and Scrum Masters to keep the backlog refined, sequenced, and ready for delivery within agile ceremonies. - Communicate roadmap, tradeoffs, experimentation outcomes, and innovation findings clearly to both technical teams and business/executive stakeholders. - Collaborate with peer Product Managers, Architecture, and Governance teams to ensure new automation, data science, and AI products integrate cleanly with core and steady-state data platforms. We'll put each bullet as

• . Now Requirements (Must Haves): List: - Minimum 5 years of experience as a Product Manager or Product Owner with a data science, automation, AI/ML, or innovation focus. - Property & Casualty (P&C) insurance industry experience. - Experience working within a Data organization (e.g., data science, AI/ML engineering, intelligent automation, analytics, or data platform teams). - Familiarity with data science, machine learning, and AI product development lifecycles, from ideation and proof of concept through production deployment. - Deep analytical skills and working knowledge of SQL, with the ability to independently query, investigate, and validate data supporting model and automation performance. - Demonstrated ability to translate ambiguous business problems into scoped product initiatives and to evaluate the business value and feasibility of emerging AI, automation, and data science capabilities. - Experience driving horizon scanning or innovation evaluation processes — proactively identifying, assessing, and prioritizing emerging tools, techniques, and use cases. - Strong verbal and written communication skills, with the ability to translate between technical and business audiences, including at the executive level. - Experience working with cross-functional agile teams, facilitating consensus on scope, design, and prioritization decisions. Now Qualifications (Nice To Haves): - Hands-on familiarity with intelligent automation platforms (e.g., IA and RPA tools), MLOps/model lifecycle tooling, and cloud AI/ML services (e.g., Azure AI, Databricks, GEAP, AWS SageMaker). - Exposure to GenAI and large language model (LLM)-based product development, including prompt engineering, agentic workflows, or responsible AI practices. - Background in data science, analytics, or engineering that provides technical credibility with model-building and automation teams. - SAFe POPM, CSPO, or equivalent agile product management certification. - Experience