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About This Role
Position Type Full Time Career area Design Location 1 Bowerman Drive, Beaverton, Oregon 97005, United States Job ID R-94583* Become a Part of the NIKE, Inc. Team
NIKE, Inc. does more than outfit the world’s best athletes. It is a place to explore potential, obliterate boundaries and push out the edges of what can be. The company looks for people who can grow, think, dream and create. Its culture thrives by embracing diversity and rewarding imagination. The brand seeks achievers, leaders and visionaries. At NIKE, Inc. it’s about each person bringing skills and passion to a challenging and constantly evolving game.
The Nike Sport Research Lab (NSRL) is a multidisciplinary team of researchers, innovators, scientists, data scientists, and engineers who lead with science to make athletes* measurably better. We deliver validated insights and capabilities that inform the future of Nike products and services.
WHO YOU’LL WORK WITH
The Lead Data Scientist partners with researchers in biomechanics, physiology, perception, and related disciplines, along with data scientists, engineers, product managers, and other innovation partners. This role reports to the Director of Data Science and provides hands-on technical leadership within multidisciplinary project teams.
- If you have a body, you are an athlete.
WHO WE ARE LOOKING FOR
Nike Sport Research Lab is looking for an experienced Data Scientist who combines strong algorithmic skills with practical project leadership and genuine curiosity about human movement and performance. This person can independently solve difficult technical problems, guide project-level decisions, and help other contributors deliver high-quality work. They understand that sensing and machine-learning systems are only useful when their outputs can be connected to the physical and scientific realities they are intended to represent.
The successful candidate is comfortable working with ambiguous research and innovation questions, translating them into rigorous analytical plans, and collaborating across scientific and technical disciplines. They remain hands-on in analysis and software development while communicating assumptions, limitations, trade-offs, and findings clearly. This is a Lead role focused on technical execution, project coherence, and mentorship, rather than organization-wide data science strategy.
- Master’s degree in Computer Science, Data Science, Statistics, Engineering, Biomechanics, Kinesiology, Applied Mathematics, Physics, or a related field. Will accept any suitable combination of education, experience and training.
- 6+ years of relevant applied experience post-degree in data science, machine learning, statistical modelling, signal processing, computer vision, or a related technical field, including ownership of complex projects.
- Strong Python proficiency and experience building tested, maintainable, reproducible analytical software using modern version control, code review, and development practices.
- Experience working with complex measurement data, such as time-series signals, IMUs, wearable sensors, image or video data, camera-based systems, or multimodal datasets, with rigorous signal processing, computer vision model evaluation and validation.
- Strong communication and collaboration skills.
- Experience with human movement, biomechanics, physiology, sport science, pose estimation, sensor fusion, and cloud data platforms is strongly preferred.
WHAT YOU’LL WORK ON
You will provide hands-on technical leadership for complex projects that use sensor and camera data to understand human movement and performance. You will develop rigorous analytical solutions, guide project execution, and work closely with scientific and engineering partners to deliver credible, useful, and reusable outcomes.
- Apply machine learning, computer vision, signal processing, statistical modeling, and related methods to problems in human movement and athletic performance.
- Develop analytical workflows using data from IMUs, wearable sensors, camera-based systems, computer vision pipelines, and other measurement technologies.
- Connect algorithm outputs to the movement or performance phenomena they represent, making assumptions, limitations, uncertainty, and failure modes explicit.
- Lead the technical execution of complex projects by clarifying questions, defining analytical plans, coordinating contributions, and communicating risks and tradeoffs.
- Design and evaluate models and measurement approaches using appropriate scientific, statistical, and computational validation methods.
- Build reusable datasets, software, pipelines, and documentation that support reproducible research and future project work.
- Investigate and evaluate emerging methods, technologies, and state-of-the-art research, translating promising advances into practical, scientifically credible capabilities for athlete measurement and performance analysis.
- Provide technical guidance, code and analysis review, and mentorship while communicating methods and findings clearly to technical and non-technical partners.
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.
NIKE, Inc. is a growth company that looks for team members to grow with it. Nike offers a generous total rewards package, casual work environment, a diverse and inclusive culture, and an electric atmosphere for professional development. No matter the location, or the role, every Nike employee shares one galvanizing mission: To bring inspiration and innovation to every athlete* in the world.
NIKE, Inc. is an equal opportunity employer. Qualified applicants will receive consideration without regard to race, color, religion, sex, national origin, age, sexual orientation, gender identity, gender expression, veteran status, or disability.
* * What You Can Expect OUR HIRING GAME PLAN
01 Apply
Our teams are made up of diverse skillsets, knowledge bases, inputs, ideas and backgrounds. We want you to find your fit – review job descriptions, departments and teams to discover the role for you.
02 Meet a Recruiter or Take an Assessment
If selected for a corporate role, a recruiter will reach out to start your interview process and be your main contact throughout the process. For retail roles, you’ll complete an interactive assessment that includes a chat and quizzes and takes about 10-20 minutes to complete. No matter the role, we want to learn about you – the whole you – so don’t shy away from how you approach world-class service and what makes you unique.
03 Interview
Go into this stage confident by doing your research, understanding what we are looking for and being prepared for questions that are set up to learn more about you, and your background.
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,653 AI roles we're tracking, Data Scientist positions make up 5% of the market. At NIKE, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
NIKE AI Hiring
NIKE has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Beaverton, OR, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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