Director, Data Science - AI

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.