AI Data Science Intern | Hybrid NYC Summer 2027

New-York-Life; New York (NY); Hybrid; USD 41,000 - 48,000; Full time

Benefits offered by this job

$3,000 bonus

Job summary

New York Life’s TDAV internship offers hands-on experience in applying data science, AI, and GenAI to business questions within a collaborative, cross-functional team. You will work with product, engineering, and business stakeholders to translate challenges into model-driven solutions, learning through a structured capstone and professional development events.

Eligibility requires pursuing a bachelor’s or master’s degree with an expected graduation in Dec 2027 or May 2028, with full-time

Qualifications

• Undergraduate or Graduate student pursuing a degree in Data Science, Computer Science, Artificial Intelligence, Statistics, or a related technical field.
• Proficiency in R, Python, SQL, traditional Machine Learning and core statistical methods including probability, hypothesis testing, and statistical inference
• Foundational understanding of generative AI principles, including LLMs, RAG, vector embeddings, prompt engineering, and LLM-based application development with pre-trained and fine-tuned models.

Responsibilities

• Focus on building and enhancing statistical and machine learning solutions, applying advanced tools and techniques to develop transformative AI and GenAI solutions for New York Life.
• Work closely with product, engineering, and business stakeholders to translate business questions into model-driven solutions.
• Contribute to the analytical model lifecycle, from problem framing and exploratory analysis through feature engineering and model validation.

Skills

R

Python

SQL

Statistics

Generative AI

LLMs

Prompt engineering

Education

Data Science or Computer Science degree

Job description

New York Life’s TDAV internship offers hands-on experience in applying data science, AI, and GenAI to business questions within a collaborative, cross-functional team. You will work with product, engineering, and business stakeholders to translate challenges into model-driven solutions, learning through a structured capstone and professional development events.

Eligibility requires pursuing a bachelor’s or master’s degree with an expected graduation in Dec 2027 or May 2028, with full-time