AI Innovation Lead - Data Science Lab

Lead AI Innovation Lead - Data Science Lab

Overview

Guardian is on a transformation journey to evolve into a modern, forward-thinking insurance company committed to enhancing the wellbeing of its customers and their families. As part of the Data & AI team, this role presents a distinctive opportunity to collaborate with the CDAO and the Head of Data Science, actively contributing to Guardian’s ongoing evolution.

Guardian’s Data & AI team spearheads a culture of data insights across Guardian, facilitating the successful realization of our strategic initiatives. Our core activities encompass creating business value from our data and analytic products. Key responsibilities include data lifecycle management, insight development, and data product delivery. We are a team of data analysts, data product owners, data scientists and most importantly, data business leaders.

Our solutions and actions are critical to Guardian’s revenue growth, risk management and customer experience.

Guardian created a Data Science Lab (DSL) to reimagine insurance in light of emerging technology, societal shifts, and evolving consumer needs. The DSL will expedite Guardian's transition to data-driven decision making and insight generation, fostering long-term innovation. The DSL will establish rapid testing capabilities for new technology and the translation of pioneering research into practical, enterprise-wide solutions.

In this role as Managing Lead Data Scientist for the DSL, you'll be entrusted with the crucial task of managing the DSL and devising AI strategy for the Data Science team. Your responsibilities will include identifying new AI capabilities, coming up with solutions to scale the Data Science team, leveraging machine learning and artificial intelligence, to drive enterprise-wide innovation across various business lines and Guardian products.

You'll collaborate with senior executives on high-impact high-visibility projects to deliver AI/ML solutions that will be market-tested and deployed to make a real difference to risk management and Guardian's overall financial performance. Successful candidates bring expertise in insurance and financial services, a passion for applying cutting-edge ML and AI insights, and the ability to design and implement data science capabilities that foster growth, competitive advantage, and customer satisfaction.

Leadership & Strategy

• Lead the Data Science Lab (DSL) by setting strategic direction, driving innovation, and ensuring alignment with Guardian Life’s business goals and digital transformation initiatives.
• Manage and mentor a high-performing team of data scientists, fostering a culture of continuous learning, experimentation, and excellence.
• Monitor developments across the AI and data science community to position Guardian as a leader in responsible and effective AI adoption.
• Report and communicate progress, opportunities, and risks to executive stakeholders, delivering insights and recommendations in a clear, actionable manner.

Innovation & Research

• Stay current with advancements in machine learning (ML), artificial intelligence (AI), and industry trends to identify and evaluate emerging capabilities and technologies.
• Collaborate with academic and industry partners to develop and test novel AI/ML methods and approaches.
• Introduce and apply ML/AI innovations to practical, high-impact opportunities across Guardian’s business units.
• Create patentable technologies and methodologies that drive long-term innovation.

AI/ML Development

• Build and lead experimentation efforts to validate the feasibility and impact of new data science techniques in real-world insurance use cases.
• Develop capabilities in Deep Learning, Large Language Models (LLMs), and Generative AI.
• Apply AI/ML to unstructured data sources such as insurance contracts, medical records, sales notes, and customer service logs.
• Deliver solutions that enhance underwriting risk assessment, claims auto-adjudication, and customer servicing.

Operational Excellence

• Establish scalable processes, frameworks, and governance models for efficient and responsible AI/ML development, testing, and deployment.
• Assess and…