Data Scientist, ML – Insurance Underwriting
Guardian Life
full-time
Posted on:
Location Type: Hybrid
Location: New York City • Massachusetts, New Jersey, New York, Pennsylvania • 🇺🇸 United States
Visit company websiteSalary
💰 $95,170 - $156,355 per year
Job Level
Mid-LevelSenior
Tech Stack
NumpyPandasPythonScikit-Learn
About the role
- 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 Guardian’s 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.
Requirements
- 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)
Benefits
- Health insurance
- 401(k) matching
- Flexible work arrangements
- Professional development opportunities
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
machine learningregressionclassificationclusteringensemble methodsPythonscikit-learnpandasnumpyprobability
Soft skills
problem-solvinganalytical skillsattention to detailcommunication skillscollaboration
Certifications
PhDMaster's degreeBachelor's degree