WHO WE ARE LOOKING FOR
We are looking for a Lead Data Scientist to advance state-of-the-art recommendation systems. This role sits at the intersection of machine learning, optimization, generative AI, and decision intelligence that help customers and partners discover the right products, optimize assortments, and make informed purchasing decisions. You will lead the development of innovative recommendation solutions that connect customers and partners with the most relevant products, using machine learning, optimization, and AI-assisted workflows to create personalized, data-driven experiences at scale.
You are passionate about solving complex business problems, translating ambiguous requirements into production-ready systems, and leading cross-functional teams through innovation.
You thrive in environments where data science, AIML engineering, product management, and user experience come together to create impactful customer experiences.
WHAT YOU BRING
- Master’s degree in Computer Science, Operations Research, Statistics, Applied Mathematics, Engineering, or a related field. Will accept any suitable combination of education, experience or training
- 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 leveraging machine learning frameworks such as PyTorch, 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
- Strong background in optimization techniques, including linear programming, mixed-integer optimization, reinforcement learning, and multi-objective optimization is preferred
- 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, with the ability to lead the design of scalable personalization solutions combining traditional recommendation methods, embeddings, semantic retrieval, ranking, and generative approaches.
- Experience with cloud-native ML platforms such as Databricks, AWS SageMaker
- Demonstrated ability to lead complex cross-functional initiatives
- Strong communication skills
WHAT YOU WILL WORK ON
As a Lead Data Scientist, you will lead the scientific strategy and technical execution for AI-powered recommendations. Your work will span predictive modeling, recommendation systems, optimization engines, and generative AI experiences.
Key responsibilities include:
- 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 the 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 closely with product managers, engineers, designers, and business stakeholders to define strategy, prioritize opportunities, and execute against 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
We offer a number of accommodations to complete our interview process including screen readers, sign language interpreters, accessible and single location for in-person interviews, closed captioning, and other reasonable modifications as needed. If you discover, as you navigate our application process, that you need assistance or an accommodation due to a disability, please complete the Candidate Accommodation Request Form.
Skills Required
- Master's degree in Computer Science, Operations Research, Statistics, Applied Mathematics, Engineering, or a related field, or a suitable combination of education, experience, or training
- 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
- Experience with machine learning frameworks such as PyTorch and 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
- Strong background in optimization techniques, including linear programming, mixed-integer optimization, reinforcement learning, and multi-objective optimization
- Experience building solutions using Agentic AI, large language models, tool orchestration, and automated or human-in-the-loop evaluation
- Expertise in modern recommender systems and emerging LLM or generative AI techniques
- Experience with cloud-native machine learning platforms such as Databricks and AWS SageMaker
- Ability to lead complex cross-functional initiatives
- Strong communication skills
Nike Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Nike and has not been reviewed or approved by Nike.
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Retirement Support — A 401(k) with a company match is complemented by options such as a Mega Backdoor Roth and deferred compensation for eligible earners. Financial coaching and structured savings programs support long-term financial security.
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Equity Value & Accessibility — An Employee Stock Purchase Plan with a stock discount sits alongside broad-based equity vehicles like RSUs and non-qualified stock options. These avenues expand wealth-building opportunities beyond base pay.
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Parental & Family Support — Paid parental leave includes maternity and paternity time, recently expanded in the U.S. to 16 weeks and extended to part-time retail teammates. Additional supports include childcare assistance at select locations and family-building benefits such as fertility, surrogacy, and adoption.
Nike Insights
What We Do
At NIKE, Inc., we innovate to serve athletes*. Every teammate - from coder to creator - plays a role in making these athletes’ dreams real. Our tech, data, and digital teams push limits every day, building the future of sport and the tools that drive it. Here, curiosity is fuel. Innovation is the game plan. Different perspectives keep us on the offense. You’ll solve challenges worth tackling, grow fast and belong to a team that backs you to do the right thing. Bring your drive. Bring your bold. Let’s move the world, together. Take your first step at nike.com/careers * If you have a body, you’re an athlete.
Why Work With Us
At NIKE, Inc., your dedication fuels the future of sport. There is a sense of pride that comes from representing an iconic brand and shaping its future. Here, we treat every day as a new opportunity to push boundaries, ask tough questions and share whole-hearted convictions. We are a team – united by the belief that anything is possible.
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