AI Product Scientist, Decision Intelligence (Hybrid)

New York Life; New York (NY); Hybrid; USD 100,000 - 143,000; Full time

Job summary

New York Life is seeking an AI Product Scientist to own a product domain from model development to roadmap planning, combining data science with product strategy. You’ll work closely with engineers, designers, and business stakeholders to translate data into actionable intelligence and measurable outcomes.

The role emphasizes applied ML, experimentation, and the development of AI-powered recommendations to enhance decision-making across field teams.

Qualifications

• Bachelor's or higher in a quantitative field; advanced degree preferred.
• Experience building production ML models that influence business outcomes.
• Strong Python and SQL for large-scale data analysis and experimentation.
• Experience designing and evaluating experiments (A/B, holdout, causal inference).
• Ability to communicate complex concepts to technical and non-technical audiences.

Responsibilities

• Design, develop, deploy, and monitor ML models and behavioral signal frameworks powering decision intelligence products.
• Lead experimentation efforts with hypothesis testing, measurement strategy, and causal analysis.
• Develop and optimize generative AI solutions, including prompts, personalization logic, and content workflows.
• Collaborate with product leaders to define priorities, roadmaps, and scalable AI/analytics solutions.
• Contribute to shared ML platforms and enterprise intelligence capabilities across products.

Skills

Python

SQL

Experimentation design

Cross-functional collaboration

Communication

Education

Bachelor's degree in Data Science/CS/Statistics/Applied Math/Engineering

Tools

Databricks

MLflow

dbt

Job description

New York Life is seeking an AI Product Scientist to own a product domain from model development to roadmap planning, combining data science with product strategy. You’ll work closely with engineers, designers, and business stakeholders to translate data into actionable intelligence and measurable outcomes.

The role emphasizes applied ML, experimentation, and the development of AI-powered recommendations to enhance decision-making across field teams.