Data Scientist- Underwriting Analytics

We need to output HTML fragment with sections for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections where there is real specific content. We need to parse the posting. First, there is a preamble: "Why join the Novacore team? ..." This is about the company, maybe "About the role"? Actually "About the role" could be description of role. The posting includes a description of the role: "Data Scientist- Underwriting Analytics Novacore's actuarial team ...". That is the role description. So we can put that under

About the role

with paragraphs. Then "Responsibilities" section: bullet list under that heading. Then "Qualifications" heading: bullet list. Then maybe "Skills" could be extracted from qualifications? The qualifications list includes many items: Strong grounding..., Data science..., P&C..., Strong SQL..., Familiarity..., You already use AI..., Curiosity..., Willingness..., A track record..., Comfort..., etc. Those could be considered qualifications. The posting also has "You Might Be a Great Fit If..." which could be considered "Skills" or "Preferred qualifications". We could treat that as "Skills" section. Benefits: earlier there is a bullet list: "We Offer A collaborative, results-driven environment Competitive compensation and comprehensive benefits Year-round social and community events Ongoing mentorship and professional development Endless opportunities for upward mobility". That's benefits. So we can have Benefits section with those bullet points. Pay: Not mentioned explicitly. No salary. So we should NOT include Pay section. Schedule: Not mentioned. So no Schedule. We must not include placeholders like "commensurate with experience". Not present. We must remove boilerplate: cookie/navigation/legal/EEO etc. Not present. We must not repeat job title as heading. So no heading for "Data Scientist- Underwriting Analytics". We'll just include content. We need to preserve all substantive information. So we need to include the introductory motivational text? That is part of "Why join the Novacore team?" That's about company culture, could be part of About the role? Might be better to include as part of About the role or maybe a separate "About the company"? But only allowed headings are the list. We could put that under About the role as introductory paragraph. We need to ensure we include all bullet points from Responsibilities, Qualifications, Benefits, and the "You Might Be a Great Fit If..." as Skills. Let's extract. Responsibilities bullet points (from the text): - Guideline Codification & Gap Detection - Translate underwriting guidelines for every program, currently spread across PDFs, institutional memory, and individual underwriters' heads, into structured, version-controlled rule sets. - Build gap analyses comparing what guidelines say against what's actually being bound, priced, and retained. - Building the Score - Take the actuarial team's loss cost and target loss ratio for a program and partner with actuarial to build underwriting models that allow program underwriters to find the best quality risks. - Apply modern data science techniques (predictive modeling, GLMs, gradient boosting, clustering) to build underwriting models and find segmentation opportunities the current rating plan misses. - Leverage AI tools (LLMs, agentic workflows, Claude) to automate and augment guideline-codification and gap-analysis work. - Pulling the Levers - Translate the underwriting models into clear underwriting action: eligibility gates, class-level appetite, terms (limits, exclusions, deductibles, endorsements), and pricing flexibility (within approved actuarial guidelines) that hit the target loss ratio while staying competitive. - Partner directly with your underwriting leaders to socialize findings and get guideline or lever changes adopted, not just documented. - Feed adopted changes and their measured impact back into the actuarial team's next loss pick. This is a two-way loop, not a one-way handoff: you don't override the actuarial number; you make sure underwriting can act on it. - Cross-Functional & Infrastructure - Document findings and adopted changes so institutional knowledge compounds instead of living in one person's head. - Build reusable frameworks and tooling that extend across every program, not one-off analyses. - Partner with the product team and actuarial team to form a cross functional pod that collectively owns the profitability of assigned programs. You will exist as one team with one voice. Collaboration, partnership and team work are critical to the success of the pod. We need to format as

• for each bullet. Could nest? Probably just flat list items; but we can include sub-bullets as separate
• with dash? Simpler: each bullet as
• . We'll keep the sub-bullets as separate
• items. Qualifications bullet points: - Strong grounding in mathematical statistics, actuarial science, and practical insurance/underwriting fundamentals. - Data science, Actuarial or actuarial-adjacent background (2+ actuarial exams passed, or an equivalent quantitative pricing credential). - You'll work constantly with actuarial output, but this is not an actuarial pricing seat. - P&C insurance experience, commercial lines and/or MGA/program business preferred. - Strong SQL and Python, with hands-on GLM / predictive modeling experience applied to underwriting or pricing, not exclusively reserving. - Familiarity with modern data tools: version control (Git), cloud platforms, BI tools (Power BI, Tableau), or notebook environments (Jupyter). - You already use AI tools in your daily workflow. - Curiosity about how LLMs and agentic frameworks can automate guideline codification and gap-analysis work. - Willingness to prototype, experiment, and ship, not just theorize. - A track record of presenting analytical findings to underwriters and getting them adopted, including handling pushback, across more than one stakeholder relationship at a time. - Comfort operating in ambiguity. Skills (You Might Be a Great Fit If...): - You've built a GLM in Python or R and watched an underwriter actually change a decision because of it, not just admired the fit statistics. - You've turned a rate need into an actual guideline or appetite change, not just a slide. - You've used an LLM to speed up something tedious (guideline extraction, report drafting) and it changed how you think about your job. - You want to build the underwriting decision layer for a cluster of programs, not just run someone else's rating plan. Benefits bullet points: - A collaborative, results-driven environment - Competitive compensation and comprehensive benefits - Year-round social and community events - Ongoing mentorship and professional development - Endless opportunities for upward mobility Now About the role: we need to include the paragraph(s) describing the role and the introductory motivational text. Let's craft About the role paragraph(s): First paragraph: "Why join the Novacore team? Because your next stellar chapter starts here — and we’re building something bold and meaningful. At Novacore, we’re not your average insurance company. We’re a team of driven professionals passionate about redefining the specialty insurance experience for our agents, carrier partners — and for each other. We specialize in tailored solutions for niche industries, powered by advanced analytics, modern technology and a culture of innovation. Backed by strong leadership and strategic growth initiatives, Novacore is poised to scale and lead in the specialty insurance market. But at our core, we believe it’s not just what we do — it’s how we do it and who we do it with. Recognized as a top workplace, Novacore is a place where ambition is supported, growth is continuous and culture matters. From day one, you’ll find mentorship, hands-on learning and clear paths for advancement. You’ll grow your skills, expand your expertise and become even more exceptional — because when you succeed, we all do." Then role-specific description: "Data Scientist- Underwriting Analytics Novacore's actuarial team owns the number: they set each program's loss cost, rate need, and target loss ratio. This role owns what happens next. You'll build the model that distributes the actuarial loss cost across real risk characteristics (building class, occupancy, TIV, geography, and beyond) and then design the underwriting levers that let the business hit that target loss ratio while staying competitive: guideline gates, segmentation cuts, terms, and recommendations to actuarial on new risk factors to consider for pricing. You're not setting program profitability. Actuarial does that. You're finding creative, defensible ways to make the program more profitable within it. This isn't a pricing-actuary role, and it isn't a reporting/BI role either. It's a hybrid that sits between actuarial output and underwriting action: part pricing analytics, part underwriting partnership, built to own a cluster of programs rather than one at a time. This is a hybrid opportunity from the Conshohocken, PA Home Office" We need to ensure we don't repeat the job title as heading; but we can include it in paragraph. Now we need to output only HTML fragment, no markdown fences. We'll produce:

  About the role

  Why join the Novacore team? Because your next stellar chapter starts here — and we’re building something bold and meaningful. At Novacore, we’re not your average insurance company. We’re a team of driven professionals passionate about redefining the specialty insurance experience for our agents, carrier partners — and for each other. We specialize in tailored solutions for niche industries, powered by advanced analytics, modern technology and a culture of innovation. Backed by strong leadership and strategic growth initiatives, Novacore is poised to scale and lead in the specialty insurance market. But at our core, we believe it’s not just what we do — it’s how we do it and who we do it with. Recognized as a top workplace, Novacore is a place where ambition is supported, growth is continuous and culture matters. From day one, you’ll find mentorship, hands-on learning and clear paths for advancement. You’ll grow your skills, expand your expertise and become even more exceptional — because when you succeed, we all do.

  Data Scientist- Underwriting Analytics Novacore's actuarial team owns the number: they set each program's loss cost, rate need, and target loss ratio. This role owns what happens next. You'll build the model that distributes the actuarial loss cost across real risk characteristics (building class, occupancy, TIV, geography, and beyond) and then design the underwriting levers that let the business hit that target loss ratio while staying competitive: guideline gates, segmentation cuts, terms, and recommendations to actuarial on new risk factors to consider for pricing. You're not setting program profitability. Actuarial does that. You're finding creative, defensible ways to make the program more profitable within it. This isn't a pricing-actuary role, and it isn't a reporting/BI role either. It's a hybrid that sits between actuarial output and underwriting action: part pricing analytics, part underwriting partnership, built to own a cluster of programs rather than one at a time. This is a hybrid opportunity from the Conshohocken, PA Home Office

  Then Responsibilities:

  Responsibilities

  • Translate underwriting guidelines for every program, currently spread across PDFs, institutional memory, and individual underwriters' heads, into structured, version-controlled rule sets.
  • Build gap analyses comparing what guidelines say against what's actually being bound, priced, and retained.
  • Take the actuarial team's loss cost and target loss ratio for a program and partner with actuarial to build underwriting models that allow program underwriters to find the best quality risks.
  • Apply modern data science techniques (predictive modeling, GLMs, gradient boosting, clustering) to build underwriting models and find segmentation opportunities the current rating plan misses.
  • Leverage AI tools (LLMs, agentic workflows, Claude) to automate and augment guideline-codification and gap-analysis work.
  • Translate the underwriting models into clear underwriting action: eligibility gates, class-level appetite, terms (limits,