Data Scientist, ML

Job Title: Data Scientist, Machine Learning (insurance)

Location: New York City | Holmdel, NJ | Bethlehem, PA | Boston (Hybrid - 3 days/week in office)

About The Company

We are a forward-thinking insurance company on a transformation journey, committed to enhancing the wellbeing of our customers and their families. 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—expediting our transition to data-driven decision making and fostering innovation by rapidly testing, scaling, and operationalizing state-of-the-art AI.

Job Summary

We are looking for a Data Scientist, Machine Learning to join our Data Science Lab. The ideal candidate will be an experienced individual contributor with a strong background in data science and machine learning, with 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

• ML Solution Design & Implementation : Design and implement machine learning solutions that automate business workflows, improve decision-making, and enhance customer and employee experiences across underwriting, claims, and customer servicing.
• Model Development & Deployment : 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 to develop robust, scalable, and production-ready models.
• Research Translation : Translate research in machine learning and statistical modeling into practical applications for underwriting automation, claims automation, customer servicing, and risk assessment that deliver measurable business outcomes.
• Cross-Functional Collaboration : Work closely with product owners, data engineers, MLOps teams, and business stakeholders to define use cases, design solutions, measure impact, and ensure models are scalable, robust, and production-ready.
• Framework Development & Governance : Contribute to building reusable components and frameworks for developing and deploying ML solutions while adhering to model governance, documentation, testing, and other best practices in partnership with key stakeholders.
  
  

Requirements

• Education:
• 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: 2+ years of hands-on experience in ML modeling and development
• Skills:
• 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)
• Other: Must be legally authorized to work in the United States without need for employer sponsorship now or in the future
  

Preferred Qualifications

• Experience in the insurance industry
• Background in insurance underwriting