Director, Advanced Analytics

MAPFRE•Webster, MA

1d•Hybrid

About The Position

Mapfre is seeking a visionary Director of Advanced Analytics to lead enterprise-wide AI strategy and execution. This role blends deep actuarial expertise, advanced analytics, business acumen, and leadership to deliver impactful, data-driven solutions across underwriting, product, pricing, and more. You’ll oversee a high-performing team of data scientists while remaining personally hands-on in model design, analytical problem solving, and technical reviews, partner closely with Product, Underwriting, and Actuarial leadership, and champion a culture of innovation, responsible AI, and continuous learning. From shaping the AI roadmap to actively building and reviewing models and delivering measurable business value, you’ll be at the forefront of transforming how actuarial and technical decisions are made through data, advanced analytics and AI.

Requirements

Actuarial background required; credentialed actuary (ACAS/FCAS or equivalent) strongly preferred.
PhD (preferred) or Master’s in a quantitative field such as Actuarial Science, Statistics, Mathematics, Engineering, Computer Science, or related quantitative field.
Minimum of 8 years of experience across actuarial, advanced analytics, or data science roles, including significant hands-on model development experience.
Hands-on experience in any of these items: pricing, rate indications, product optimization, actuarial forecasting, loss modeling, profitability analysis, underwriting, risk selection and portfolio management.
3+ years of leadership experience managing high-impact data science or cross-functional teams (preferred), with continued personal technical contribution.
Proven track record in the P&C insurance industry, particularly within Product, Pricing, Underwriting, or Risk domains.
Advanced proficiency in Python and SQL for data manipulation and model development, with recent, ongoing hands-on usage.
Deep expertise in actuarial methods, machine learning, statistics, and MLOps practices.
Hands-on experience building, deploying, and maintaining scalable analytics models on large insurance datasets.
Skilled in project scoping, planning, and delivery of robust, business-aligned AI solutions.
Experience with GenAI, NLP, and advanced modeling techniques across structured and unstructured data.
Strong grasp of model governance, responsible AI, and regulatory compliance.
Excellent communicator with the ability to translate complex technical insights for diverse audiences.
Strong influencing, negotiation, and conflict resolution skills.

Responsibilities

Lead AI Strategy & Execution: Align enterprise-wide advanced analytics initiatives with business priorities, with a strong emphasis on product strategy, pricing, underwriting, while actively contributing to the design and implementation of key models that drive measurable impact.
Drive Cross-Functional Collaboration: Build and manage a structured AI engagement framework that enables close partnership between Actuarial, Product, Underwriting, Data Engineering, and IT, ensuring while serving as a hands-on technical partner in deep-dive working sessions and solution design.
Champion AI Literacy & Innovation: Promote data-driven and model-informed decision-making within technical areas by educating the Technical Area stakeholders, leading by example through hands-on analytical work, and fostering experimentation beyond traditional actuarial approaches.
Translate Business Needs into Scalable AI Solutions: Lead and directly contribute to the development of advanced analytics and AI use cases, from problem framing to solution design and performance monitoring, ensuring solutions complement actuarial judgment while improving predictive power, efficiency, and speed to market.
Oversee Full Analytics Project Lifecycle: Manage planning, execution, validation, and maintenance of analytics projects while personally reviewing model assumptions, methodology, performance, and limitations, ensuring alignment with actuarial standards, model risk management, regulatory expectations, and responsible AI principles.
Advance MLOps & Responsible AI Practices: Implement best practices with direct hands-on involvement on model development, deployment, monitoring, and retraining with a focus on fairness, explainability, and regulatory compliance.
Expand AI Capabilities Through Partnerships: Collaborate with internal teams and external partners, while maintaining internal technical ownership and the ability to independently assess, stress-test, and challenge vendor models.
Lead & Develop High-Performing Teams: Provide strategic leadership and mentorship to analytics teams, fostering a culture where leaders remain technically credible, hands-on, and actively involved in problem solving.