Lead Data Scientist

Nike • Beaverton, OR

1d

About The Position

The Lead Data Scientist leads scientific strategy and technical execution for AI-powered recommendations. The role spans predictive modeling, recommendation systems, optimization engines, and generative AI experiences, developing scalable solutions that connect customers and partners with relevant products and support informed purchasing decisions. It combines machine learning, optimization, generative AI, and decision intelligence to create personalized, data-driven experiences. The position also leads cross-functional initiatives, translates ambiguous business requirements into production-ready systems, and mentors data scientists and machine learning engineers.

The role works closely with product managers, engineers, designers, and business stakeholders to define strategy, prioritize opportunities, and execute the product roadmap. It applies scientific rigor through model evaluation, offline benchmarking, online experimentation, and continuous improvement, and communicates findings and recommendations to technical and non-technical audiences, including senior leadership.

Requirements

• Master’s degree in Computer Science, Operations Research, Statistics, Applied Mathematics, Engineering, or a related field; suitable combinations of education, experience, or training will be accepted.
• 5+ years of experience leading the design, development, and deployment of data science, machine learning, optimization, and operations research models at scale.
• Strong proficiency in Python and experience with machine learning frameworks such as PyTorch or TensorFlow.
• Deep knowledge of recommendation systems, ranking models, personalization, and predictive analytics.
• Experience designing and evaluating large-scale experimentation platforms and A/B testing methodologies.
• Experience building solutions using Agentic AI, large language models (LLMs), tool orchestration, and evaluating Agentic systems using automated and human-in-the-loop approaches.
• Demonstrated expertise in modern recommender systems and emerging LLM/GenAI techniques, including the ability to lead scalable personalization solutions combining traditional recommendation methods, embeddings, semantic retrieval, ranking, and generative approaches.
• Experience with cloud-native ML platforms such as Databricks or AWS SageMaker.
• Demonstrated ability to lead complex cross-functional initiatives.
• Strong communication skills.

Nice To Haves

• Background in optimization techniques, including linear programming, mixed-integer optimization, reinforcement learning, and multi-objective optimization.

Responsibilities

• Develop and deploy advanced recommendation models that deliver personalized product experiences aligned with customer needs and partner business objectives.
• Design and implement optimization frameworks that balance customer preferences, inventory availability, profitability, and operational constraints.
• Lead development of generative AI capabilities that enhance recommendation experiences through intelligent insights, explanations, and decision support.
• Establish experimentation and measurement frameworks to evaluate recommendation quality, customer engagement, business impact, and AI effectiveness.
• Partner with product managers, engineers, designers, and business stakeholders to define strategy, prioritize opportunities, and execute the product roadmap.
• Drive scientific rigor through model evaluation, offline benchmarking, online experimentation, and continuous improvement.
• Mentor and develop data scientists and machine learning engineers, fostering technical excellence and a culture of learning.
• Communicate complex analytical findings and strategic recommendations to technical and non-technical audiences, including senior leadership.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior