Director of Insurance Analytics

MSIG Holdings•Warren, NJ

6h•$225,000 - $250,000•Hybrid

About The Position

Part of the MS&AD group, a top-10 global P&C insurance carrier, MSIG USA is on a mission to become a data-driven leader in specialty insurance, focused on ambitious and profitable growth. Our Insurance Analytics function is at the heart of this transformation – delivering analytics and data science solutions that drive impact across Underwriting, Pricing, Claims, Actuarial and beyond. We are seeking a hands-on analytics Manager/Director to lead a portfolio of high-impact initiatives, drive cross-functional delivery, and personally develop analytical solutions. This role is ideal for a builder-leader who thrives on both technical execution and stakeholder engagement. You will lead the full analytics lifecycle end-to-end: problem framing and hypothesis definition, experimentation and modeling, and operationalization into repeatable decision support with monitoring and sustained adoption. Success is measured by measurable business outcomes, not slideware.

Requirements

Bachelor’s degree in Statistics, Computer Science, Mathematics, Economics, or a related quantitative field or equivalent practical experience.
7+ years of experience in analytics or data science, with a track record of delivering measurable business impact.
2+ years leading analytics initiatives and/or mentoring technical team members in a senior/lead capacity.
Proficiency in Python and SQL; ability to write production-quality analytical code and review others’ work.
Strong communication skills with ability to influence senior stakeholders; translate technical work into clear decisions, actions, and tradeoffs

Nice To Haves

Bachelor’s or Master’s degree in Statistics, Mathematics, Computer Science, Engineering, Economics, or a related field (or equivalent practical experience).
Property & Casualty insurance experience (commercial lines preferred) or demonstrated ability to ramp quickly in complex domains.
Experience productionizing analytics/model outputs in partnership with engineering teams (e.g., APIs/services, CI/CD, containerization).
Experience with cloud-based analytics environments and modern ML frameworks (e.g., scikit-learn, XGBoost).
Experience with experimentation, causal inference, or rigorous measurement approaches in business settings.
Experience establishing monitoring practices appropriate to the solution (e.g., model performance, data quality, usage/adoption).
Familiarity with BI tools (Power BI, Tableau) and data visualization best practices.
Track record of mentoring, coaching, and/or managing technical teams.

Responsibilities

Lead a portfolio of analytics & data science initiatives
Own a portfolio of initiatives end-to-end: problem framing, prioritization, delivery planning, stakeholder alignment, and measurable business outcomes.
Translate ambiguous business questions into clear problem statements, hypotheses, and structured analytical approaches that influence decisions and operations.
Define success metrics and measurement plans (including benefit estimation) and track adoption and impact over time.
Drive cross-functional delivery across Underwriting, Pricing, Claims and Actuarial; ensure solutions are practical, adopted, and aligned to business priorities.
Partner closely with business leaders to align work to decision needs, manage tradeoffs transparently, and maximize impacts
Contribute directly by writing and reviewing code (Python & SQL), building analytical prototypes, reusable analytics assets, and decision-support tools, and unblocking technical decisions.
Apply statistical and machine learning techniques (e.g., regression, classification, clustering, time series) to solve business problems and support decision-making.
Develop and deploy solutions in a cloud-based analytics environment, partnering with platform and engineering teams to operationalize outputs into business workflows and drive sustained adoption.
Design and execute experiments and tests (as appropriate to the use case) to evaluate interventions, quantify impact, and support decision-making.
Define and reinforce production-minded practices and standards for analytical quality appropriate to the use case: validation, testing/controls, documentation, reproducible code, and operational transition plans for ongoing use.
Establish monitoring expectations appropriate to the solution (e.g., usage, data quality, and model performance) and drive continuous improvement based on signals.
Deliver decision support for Underwriting and Pricing/Actuarial
Lead and contribute to solutions that improve underwriting and pricing decisions (e.g., portfolio diagnostics, segmentation and feature development, workflow decision support), partnering closely with business leaders to drive adoption and measurable impact.
Support implementation into decisions and processes by educating stakeholders on model/analysis intent, limitations, and appropriate use, and by providing practical documentation and tooling.
Communicate findings clearly to both technical and non-technical audiences, and translate insights into concrete actions, process changes, or product improvements.
Partner with Claims leadership to identify and deliver high-value analytics and decision support opportunities that improve operational effectiveness and outcomes.
Support the data enablement needed for claims analytics in partnership with data and technology teams; translate claims workflows into analytics-ready measures and repeatable decision support and reporting.
Where applicable, help operationalize and monitor analytical solutions used in claims decisioning and workflows.
Serve as a trusted thought partner to senior stakeholders; communicate clearly, facilitate working sessions, and manage tradeoffs transparently.
Produce executive-ready narratives that connect analytical evidence to decisions, risks, and tradeoffs.
Mentor data scientists/analysts on problem structuring, technical approach, and effective communication.
Over time, help build the team through hiring, onboarding, and talent development as scope expands; responsibilities may evolve into formal people leadership.
Collaborate with technology and data platform teams to enable responsible deployment and operation of analytics solutions (access, security, maintainability, and monitoring expectations appropriate to the solution), without owning enterprise platforms.
Help define reusable patterns for analytics delivery (templates, review checklists, documentation standards) that reduce friction and improve time-to-value.