Director, Risk Control Business Analysis

Liberty Mutual Insurance•Boston, MA

About The Position

Lead a team of business analysts who own data management, operational and value reporting, and ad‑hoc data requests for Risk Control. This role is central to reinventing Risk Control through AI and advanced analytics: you will set strategy, partner with data science and engineering to deliver productionized models and insights and translate those capabilities into underwriting and claims outcomes. Balance long‑term strategic thinking (roadmap, governance, value framework) with hands‑on execution (use‑case definition, prototype-to-production delivery, change management). Represent Risk Control in cross‑functional AI and data conversations, and build a data‑savvy, outcome‑driven analytics practice that standardizes approaches across the function and accelerates measurable underwriting improvement.

Requirements

Advanced to expert level knowledge of data sources, tools, applications and business drivers.
Advanced knowledge of business operations, policies, procedures and priorities, to include a strong understanding of the function's value chain and market conditions.
Solid ability to select, develop and engage employees.
Strong ability to exercise influence and build consensus; communication skills;
Promote collaboration, teamwork and change initiatives; and to build value for customers through a service orientation, innovation, and continuous improvement.
Displays sound business acumen and integrated thinking.
Competencies typically acquired through a Bachelor's degree or equivalent experience.
Minimum of 10 years of relevant experience to include business analysis work and prior experience working in an effective leadership and/or management capacity.

Nice To Haves

Demonstrated strategic leadership in analytics or AI transformations, with the ability to set vision and deliver measurable business outcomes.
Track record of partnering with data science and engineering to move models from prototype to production in regulated or enterprise environments.
Deep understanding of Risk Control / Risk Engineering and Property & Casualty insurance operations, and experience applying analytics to underwriting and claims outcomes.
Practical experience with BI and visualization tools (Power BI, Tableau) and a working knowledge of ML concepts, model risk controls, and production monitoring.
Strong business case development skills—ability to prioritize use cases by value, cost, complexity, and risk.
Proven change‑management and stakeholder engagement capabilities; able to influence senior leaders and drive cross‑functional adoption.
Excellent communication: translate complex analytics into clear narratives and actionable recommendations for commercial and technical audiences.
Commitment to responsible data and AI practices, including privacy, security, governance, and bias mitigation.
Advanced degree or certifications in analytics, data science, AI, or a related field are strongly preferred.

Responsibilities

Define and own the Risk Control analytics and AI roadmap, aligned to business strategy and measurable value (loss improvement, retention, efficiency, cycle time).
Lead strategic discovery: identify high‑value enterprise and partner use cases (underwriting, claims, product), develop business cases, and prioritize work by expected impact and deliverability.
Partner closely with Data Science and IT to design, validate, and operationalize models and AI/ML solutions—ensuring production readiness, monitoring, explainability, and appropriate controls.
Drive end‑to‑end delivery: scope requirements, lead prototyping, oversee production deployment, and embed insights into workflows and decision processes for underwriting, risk engineers, and claims teams.
Operate as a cross‑functional convenor—engage Underwriting, Claims, Product, Data Science, IT, and external partners to align priorities, share data, and accelerate adoption of analytics.
Build and maintain a robust measurement framework and value tracking process that quantifies ROI from analytics and AI; iterate on interventions based on measured outcomes.
Set and enforce data standards, definitions, governance, and quality practices to create a trusted single source of truth for Risk Control.
Champion responsible AI: embed privacy, security, bias mitigation, explainability, and monitoring in analytics lifecycles.
Enable self‑service and democratized insights by setting UX/documentation standards, training stakeholders, and building scalable analytics products.
Recruit, mentor, and develop a multi‑disciplinary analytics team (data analysts, analytics engineers, and product-focused roles); ensure the team has both strategic and execution capabilities.
Lead change management and stakeholder adoption plans—translate analytics into practical playbooks, operating procedures, and training to change behaviors and improve outcomes.