Data Scientist

Novacore•Conshohocken, PA

19h

About The Position

The Data Scientist will work closely with underwriting, product, operations, and leadership teams and expected to own problems end-to-end. The Data Scientist will operate with a high degree of independence while collaborating closely with cross-functional stakeholders. This role draws on the type of work seen across the broader insurance ecosystem. This role will report to the SVP, Data & AI.

Requirements

3+ years of experience in a data science, analytics, or quantitative research role — experience in the P&C insurance space (carrier, MGA, broker, or insurtech) strongly preferred.
Bachelor’s degree in a quantitative discipline (Statistics, Mathematics, Computer Science, Actuarial Science, or Economics); Masters degree is a plus.
Strong proficiency in Python and/or R for statistical analysis and model development.
Advanced SQL skills and proven ability to work with large, complex relational datasets across multiple source systems.
Hands-on experience building and deploying predictive models beyond proof-of-concept — including documentation, monitoring, and stakeholder handoff.
Working knowledge of core P&C insurance concepts: premium, loss ratio, combined ratio, and policy lifecycle.

Nice To Haves

Prior experience at a P&C Insurance, MGA, program administrator, or insurtech in a data or analytics role — exposure to an MGA-specific data environment (policy admin, bordereaux reporting, capacity provider data requirements) is a meaningful differentiator.
Experience building AI/ML-powered features in a production insurance product context, including prompt engineering or LLM integration for document processing or underwriting support.

Responsibilities

Analyze submission, bind, and quote data to identify trends in hit ratio, declination patterns, and appetite alignment.
Support pricing analysis and adequacy reviews in collaboration with actuarial resources or carrier partners, using exposure-normalized loss data.
Partner with product and distribution teams to build funnel analytics, conversion models, and cohort analysis.
Develop customer lifetime value (LTV) and retention models to support renewal strategy and identify at-risk accounts before they lapse.
Analyze distribution partner performance data to identify growth opportunities, cross-sell potential, and capacity allocation priorities.
Partner with engineering and IT to build and maintain reliable data pipelines from multiple sources including policy admin systems, claims platforms, and third-party data enrichment providers (e.g., LexisNexis, Verisk, CoreLogic).
Ensure data quality, governance, and documentation standards are upheld across all analytical datasets.
Contribute to the buildout of a scalable analytics infrastructure, including data warehouse design and BI tooling integration.
Design and maintain dashboards and self-service reporting tools that give underwriting, operations, and leadership teams real-time visibility into KPIs.
Translate complex quantitative findings into plain-language narratives for non-technical audiences including underwriters, distribution partners, and executive leadership.
Prepare regular performance reports for capacity providers and carrier partners, ensuring data accuracy and consistency across all external reporting.
Develop self-service reporting tools and dashboards using BI platforms.