Data Scientist- Underwriting Analytics

Novacore Insurance Services, LLC; Conshohocken (Montgomery County); Hybrid; USD 120,000 - 160,000; Full time

Benefits offered by this job

Competitive compensation

Comprehensive benefits

Mentorship and professionaldevelopment

Year-round social events

Upward mobility

Job summary

Novacore Insurance Services, LLC is seeking a Data Scientist - Underwriting Analytics to bridge actuarial output and underwriting action. You will codify guidelines, develop underwriting scores, and translate models into actionable terms and pricing decisions.

The role sits hybrid from Conshohocken, PA, with collaboration across actuarial, product, and underwriting teams. You’ll leverage modern data tools, build predictive models, and apply AI frameworks to automate guideline codification and

Qualifications

• 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.

Responsibilities

• Guideline Codification & Gap Detection: Translate guidelines into structured rule sets and build gap analyses.
• Building the Score: Combine loss cost and target loss ratio with actuarial to build underwriting models using GLM, predictive modeling, clustering.
• Pulling the Levers: Translate models into underwriting actions—gates, appetite, terms, and pricing flexibility.
• Cross-Functional & Infrastructure: Document findings, build reusable frameworks, and partner with product and actuarial teams.

Skills

SQL

Python

GLM

Predictive modeling

Clustering

AI tools

LLMs

Agentic workflows

Data visualization

Education

2+ Actuarial exams passed

Tools

Git

Power BI

Tableau

Jupyter

Job description

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.

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

So if you're ready to be part of something extraordinary - with a team that's transforming commercial insurance - we want to meet you.

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.

This role owns what happens next. 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

Responsibilities

• 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.

Qualifications

• 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.

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.