Director of Insurance Analytics

MSIG USA•Cincinnati, OH

8d•$225,000 - $250,000•Hybrid

About The Position

MSIG USA is seeking a hands-on analytics leader to advance its goal of becoming a data-driven leader in specialty insurance. The role leads a portfolio of high-impact analytics and data science initiatives across Underwriting, Pricing, Claims, Actuarial, and related functions, while personally developing analytical solutions. It owns the analytics lifecycle from problem framing and hypothesis definition through experimentation, modeling, operationalization, monitoring, and sustained adoption. Success is measured by business outcomes. The role partners with business leaders and technology, data, platform, and engineering teams to turn complex business questions into practical decision support. It contributes technically through Python and SQL coding, prototypes, reusable analytics assets, and tools, and applies statistical and machine learning methods to business problems. The position also develops delivery standards, communicates findings to technical and non-technical audiences, and mentors analysts and data scientists. People-management scope may vary based on the final level and organizational needs. The position is hybrid, with four days in the office; remote work may be considered in exceptional circumstances.

Requirements

Bachelor’s degree in Statistics, Computer Science, Mathematics, Economics, or a related quantitative field, or equivalent practical experience.
At least 7 years of experience in analytics or data science, with a track record of delivering measurable business impact.
At least 2 years leading analytics initiatives and/or mentoring technical team members in a senior or lead capacity.
Proficiency in Python and SQL, including the ability to write production-quality analytical code and review others’ work.
Strong communication skills and the ability to influence senior stakeholders and 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 and Casualty insurance experience, preferably in commercial lines, or demonstrated ability to ramp quickly in complex domains.
Experience productionizing analytics or model outputs with engineering teams, such as through APIs or services, CI/CD, or containerization.
Experience with cloud-based analytics environments and modern machine learning frameworks such as scikit-learn or XGBoost.
Experience with experimentation, causal inference, or rigorous measurement approaches in business settings.
Experience establishing monitoring practices appropriate to a solution, including model performance, data quality, and usage or adoption.
Familiarity with BI tools such as Power BI or Tableau and data visualization best practices.
Track record of mentoring, coaching, and/or managing technical teams.

Responsibilities

Lead a portfolio of analytics and data science initiatives from problem framing and prioritization through delivery, stakeholder alignment, and measurable business outcomes.
Translate ambiguous business questions into problem statements, hypotheses, and structured analytical approaches.
Define success metrics and measurement plans, estimate benefits, and track adoption and impact over time.
Drive cross-functional delivery across Underwriting, Pricing, Claims, and Actuarial, ensuring solutions are practical, adopted, and aligned with business priorities.
Partner with business leaders to connect analytics work to decision needs, manage tradeoffs transparently, and maximize impact.
Write and review Python and SQL code; build analytical prototypes, reusable analytics assets, and decision-support tools; and unblock technical decisions.
Apply statistical and machine learning techniques, including regression, classification, clustering, and time series, to business problems and decision support.
Develop and deploy solutions in a cloud-based analytics environment with platform and engineering partners, operationalizing outputs in business workflows and supporting sustained adoption.
Design and execute experiments and tests when appropriate to evaluate interventions and quantify impact.
Establish production-minded analytical quality practices, including validation, testing and controls, documentation, reproducible code, and operational transition plans.
Define monitoring expectations appropriate to each solution, including usage, data quality, and model performance, and drive continuous improvement.
Lead and contribute to solutions that improve underwriting and pricing decisions, including portfolio diagnostics, segmentation, feature development, and workflow decision support.
Educate stakeholders on analytical and model intent, limitations, and appropriate use; provide practical documentation and tooling.
Communicate findings to technical and non-technical audiences and translate insights into actions, process changes, or product improvements.
Partner with Claims leadership to identify and deliver analytics and decision-support opportunities that improve operational effectiveness and outcomes.
Work with data and technology teams to enable claims analytics, translate claims workflows into analytics-ready measures, and build repeatable decision support and reporting.
Where applicable, help operationalize and monitor analytics solutions used in claims decisioning and workflows.
Serve as a thought partner to senior stakeholders, facilitate working sessions, and produce executive-ready narratives connecting evidence to decisions, risks, and tradeoffs.
Mentor data scientists and analysts on problem structuring, technical approaches, and communication.
As scope expands, help build the team through hiring, onboarding, and talent development; responsibilities may evolve into formal people leadership.
Collaborate with technology and data platform teams on responsible deployment and operation, including access, security, maintainability, and monitoring expectations, without owning enterprise platforms.
Help define reusable analytics delivery patterns, such as templates, review checklists, and documentation standards.