Insurance Data Science Summer Analyst: ML/AI & Risk

KKR; New York (NY); On-site; USD 100,000 - 110,000; Full time

Job summary

KKR's 2027 Summer Analyst Program offers hands-on exposure within the Insurance Data Science group in New York, applying ML and AI to policy-level transactions and risk forecasting. You will develop models, build AI solutions, and support data pipelines, with potential for a 2028 full-time offer for top performers.

Ideal candidates are master's students in quantitative fields with strong Python/SQL skills, ML knowledge, and comfort with modern LLM tooling and Generative AI concepts.

Qualifications

• Anticipated graduation date from a master's program between December 2027 and June 2028.
• Quantitative field focused study (CS, Engineering, Mathematics, Actuarial Science or related).
• 3+ years/six semesters of programming experience.
• Proficiency in Python and SQL is required with strong foundations in data structures, statistics, and ML methods.
• Knowledge of Generative AI and modern LLM tooling (RAG pipelines, vector search, agent workflows).
• Ability to evaluate AI outputs for accuracy and relevance; proficient in documenting work.

Responsibilities

• Implement machine learning and optimization tools.
• Develop supervised and unsupervised ML models.
• Build AI solutions for the team and wider firm.
• Assist with ad hoc data analytics projects.
• Collaborate to develop the team's automated data pipeline.

Skills

Python

SQL

Machine learning

Data analysis

Communication

Teamwork

Education

Master's degree (quantitative)

Tools

Vector search

LLM tooling

Job description

KKR's 2027 Summer Analyst Program offers hands-on exposure within the Insurance Data Science group in New York, applying ML and AI to policy-level transactions and risk forecasting. You will develop models, build AI solutions, and support data pipelines, with potential for a 2028 full-time offer for top performers.

Ideal candidates are master's students in quantitative fields with strong Python/SQL skills, ML knowledge, and comfort with modern LLM tooling and Generative AI concepts.