Director, Data Science - AI

Job Title: Director, Data Science – AI

Location: New York, NY (Hybrid – 3 Days In Office)

Job Summary

As part of an Artificial Intelligence & Data organization, you will contribute to an enterprise AI-led transformation by developing and supporting the deployment of 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 ML, Gen AI, and Agentic AI, while providing technical leadership through mentoring junior data scientists and interns. You'll work in close partnership with business product owners to champion new ideas that advance AI adoption, while helping measure, articulate, and communicate business value. This role also contributes to hiring decisions for full-time employees, interns, and contractors, and builds expertise in enterprise AI practices, model lifecycle management, responsible AI principles, and modern technology ecosystems.

Key Responsibilities

Lead the execution of AI/ML and GenAI initiatives in partnership with data, technology, product, and business teams to deliver scalable solutions.
Partner with technology and business leads to redesign and operationalize business workflows that embed AI, GenAI, and Agentic solutions.
Own business-aligned OKRs for AI initiatives, ensuring efforts are outcome-driven, tied to enterprise value creation, and supported through PI planning and roadmap development.
Deliver all aspects of the full Model Development Life Cycle (MDLC), from data exploration and feature engineering to model training, validation, deployment, and performance monitoring.
Develop and operate models using cloud platforms (AWS, GCP) and modern data platforms (Snowflake, Databricks), ensuring quality, security, and scalability; partner with ML Engineers for large-scale production deployment and scaling.
Implement and refine LLM/RAG approaches including vector stores, embeddings, retrieval optimization, and prompt orchestration with evaluators for reliability.
Adhere to model governance, documentation, testing, and CI/CD best practices in partnership with MLOps.
Mentor and hire junior data scientists, interns, and vendor resources to deliver key business initiatives.
Design and evaluate agentic and AI-powered solutions that automate routine business tasks with human-in-the-loop checkpoints, escalation thresholds, and safety guardrails.
Work with AI Engineers to ensure AI capabilities are accessible through APIs, dashboards, and integrated workflow applications used by business teams.
Build lightweight UI prototypes (e.g., Streamlit) to validate usability and support adoption.
Stay informed on industry trends by engaging in relevant conferences and sharing key insights with internal and external audiences.

Requirements

Advanced degree in Computer Science, Data Science, Machine Learning, AI, Engineering, Mathematics, Statistics, or a related quantitative field.
8+ years of experience applying data science and AI/ML to real-world business problems.
2+ years of experience working directly with business stakeholders on key roadmap initiatives.
Proficiency in Python and SQL; working knowledge of core software engineering concepts including version control (Git/GitHub), testing, and logging.
Solid experience with cloud platforms including AWS and GCP (SageMaker, Bedrock, Vertex AI) and modern data ecosystems (Snowflake, Databricks).
Working knowledge of LLMs, RAG architecture, and agentic frameworks, including safe automation design and evaluation practices.
Solid grounding in ML methods including supervised and unsupervised learning, gradient boosting, deep learning, feature engineering, regularization, and cross-validation.
Ability to translate analytical findings into clear business insights and collaborate effectively with cross-functional partners.
Experience collaborating across product, data, and engineering teams to deliver scalable, production-ready AI solutions.
Ability to communicate complex ideas simply, presenting impact, trade-offs, and recommendations to non-technical partners.

Preferred Qualifications

Experience in the life insurance industry or consumer finance domains.