Associate - Data Scientist

New York Life Insurance Co•New York, NY

8d•Hybrid

About The Position

As part of the Artificial Intelligence & Data (AI&D) organization, you will contribute to New York Life’s digital transformation by developing and deploying AI and machine learning solutions that enhance efficiency and improve client, agent, and employee experiences. This role supports the development of AI solutions spanning traditional Machine Learning, Generative AI, and Agentic AI. The focus is on building core technical skills, learning enterprise AI practices, and delivering well-defined components of AI solutions. You will collaborate closely with Data Scientists, Engineers, and product and technology partners while developing an understanding of model lifecycle management, responsible AI principles, and New York Life’s technology ecosystem.

Requirements

Advanced degree in Computer Science, Data Science, Machine Learning, Artificial Intelligence, Engineering, Mathematics, Statistics, or a related quantitative field.
1–3 years of experience working with data, analytics, machine learning, or AI solutions.
Foundational knowledge of machine learning concepts, statistics, and data analysis techniques.
Foundational understanding of Generative AI concepts, including large language models and prompt engineering.
Proficiency in Python and SQL, with working knowledge of core software engineering concepts such as version control using Git or GitHub.
Strong communication skills and a collaborative, team-oriented mindset.
Curiosity, a growth mindset, and a strong interest in building a career in AI and Data Science.

Nice To Haves

Experience in the life insurance industry or consumer finance domains is a plus.

Responsibilities

Execute AI and machine learning initiatives in partnership with data, technology, product, and business teams.
Contribute to the end-to-end model lifecycle, including data exploration, feature development, model training and validation, deployment, monitoring, and iteration.
Develop and help operationalize models using AWS services such as SageMaker and Bedrock, along with modern data platforms including Snowflake and Databricks, ensuring quality, security, and scalability.
Partner with machine learning engineers to support large-scale production deployment and solution scaling.
Assist in designing and evaluating agentic and AI-powered solutions that automate routine business tasks with human-in-the-loop checkpoints, escalation thresholds, and safety guardrails.
Support the implementation and refinement of LLM and RAG approaches, including vector stores, embeddings, retrieval optimization, and prompt orchestration, with evaluation for reliability and performance.
Build lightweight UI prototypes (e.g., Streamlit) to validate usability and support user adoption.
Adhere to model governance, documentation, testing, and CI/CD best practices in collaboration with MLOps teams.