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Lead Data Scientist

Nike
Beaverton, USA
Full-time
Lead · 5+ years experience
Onsite
Discovered 4 days ago
awsdatabrickspythonpytorchsagemakertensorflow

Full Job Posting

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.

Qualifications

  • 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
  • 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.
  • 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.

Preferred Qualifications

  • Strong background in optimization techniques, including linear programming, mixed-integer optimization, reinforcement learning, and multi-objective optimization is preferred

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