Data Scientist, ML - Insurance Underwriting (GL88)

Data Scientist, Machine Learning

We're on a transformation journey to evolve into a modern, forward-thinking insurance company committed to enhancing the wellbeing of its customers and their families. This role presents a distinctive opportunity to drive real-world impact by applying cutting-edge AI to transform how we do business.

Our Data & AI team spearheads a culture of intelligence and automation across the enterprise, creating business value from advanced data and AI solutions. Our team includes data scientists, engineers, analysts, and product leaders working together to deliver AI-driven products that power growth, improve risk management, and elevate customer experience.

We created the Data Science Lab (DSL) to reimagine insurance in light of emerging technology, evolving consumer needs, and rapid advances in AI. The DSL expedites our transition to data-driven decision making and fosters innovation by rapidly testing, scaling, and operationalizing state-of-the-art AI.

We are seeking a Data Scientist, Machine Learning—an experienced individual contributor with strong background in data science & machine learning and a track record of turning advanced research into practical, impactful enterprise solutions. This role focuses on building, deploying, and scaling ML models and intelligent automation solutions that reshape how we operate, serve customers, and drive growth. You'll collaborate directly with senior executives and cross-functional teams on high-visibility projects to bring next-generation ML to life across our products and services.

Key Responsibilities

• Design and implement machine learning solutions that automate business workflows, improve decision-making, and enhance customer and employee experiences.
• Apply ML techniques (e.g., regression, classification, clustering, ensemble methods such as Random Forest and XGBoost) to structured and semi-structured data such as claims, underwriting notes, and customer records.
• Develop robust, scalable, and production-ready ML models that integrate with our platforms to deliver measurable business outcomes.
• Collaborate with data engineers and MLOps teams to ensure models are scalable, robust, and production-ready.
• Translate research in machine learning and statistical modeling into practical applications for underwriting, claims automation, customer servicing, and risk assessment.
• Work closely with product owners, engineers, and business stakeholders to define use cases, design solutions, and measure impact.
• Contribute to building reusable components and frameworks for developing and deploying ML solutions.
• Adhere to model governance, documentation, testing, and other best practices in partnership with key stakeholders.

You are

• Passionate about applying machine learning to solve real-world business challenges.
• Curious about new ML techniques and their practical applications in industry.
• A hands-on builder who enjoys moving solutions from prototype to deployment.
• Comfortable collaborating in cross-functional teams and aligning technical solutions with business goals.
• An effective communicator, able to present complex ideas and trade-offs to both technical and non-technical stakeholders.

You have

• PhD with 0–1 years of experience, Master's degree with 2+ years, or Bachelor's degree with 4+ years in Statistics, Computer Science, Engineering, Applied Mathematics, or related field.
• Experience in insurance industry (Underwriting Experience is Preferred)
• 2+ years of hands-on experience in ML modeling and development.
• Background in insurance and underwriting preferred
• Solid understanding of probability, statistics, and machine learning fundamentals.
• Strong programming skills in Python and familiarity with frameworks like scikit-learn, pandas, and numpy.
• Experience with a variety of machine learning techniques (regression, classification, clustering, ensemble methods, etc.) and their real-world advantages/drawbacks.
• Excellent problem-solving and analytical skills with attention to detail.
• Strong communication skills and ability to collaborate effectively with product and engineering teams.
• Working knowledge of core software engineering concepts (version control with Git/GitHub, testing, logging)

Location

• Three days a week at our office in New York, NY, Holmdel, NJ, Bethlehem, PA, or Boston, MA