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About This Role
Position Type
Full Time
Career area
Data
Location
1 Bowerman Drive, Beaverton, Oregon 97005, United States
Job ID
R\-89671*
Become a Part of the NIKE, Inc. Team
NIKE, Inc. does more than outfit the world’s best athletes. It’s a place where passionate individuals come together to create the future of sport. We are unapologetic about who we are and what we’re after—bringing innovation and inspiration to every athlete\* in the world. We look for athletes who can push boundaries, elevate our potential and continue leading us to greatness. The next tastemakers, playmakers, risk takers and glue players. Are you game?
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. We look for people who can grow, think, dream and create. Its culture thrives by embracing diversity and rewarding imagination. We seek achievers, leaders and innovators. At Nike, it’s about each person bringing skills and passion to a challenging and constantly evolving game.
Data science is a competitive differentiator for Nike and is fundamentally changing how the company serves athletes and consumers around the world. The Data Science \& Analytics team builds technical solutions to manage the marketplace and optimize the supply chain. Using big data, advanced analytics, and innovative technology, we strive to ensure that Nike gets the right products to the right place at the right time for the consumers.
WHO WE ARE LOOKING FOR
We are looking for an experienced Senior Data Scientist to join our Data Science \& Advanced Analytics team, which sits within Nike’s Supply Chain and Planning Technology (SCPT). This role is part of a cross\-functional Agile squad and serves a critical function in providing technical expertise and leadership in building predictive and prescriptive analytical solutions for Nike’s global supply chain and operations, specifically in the areas of manufacturing, materials sourcing, and supply planning.
The candidate needs to be a dependable teammate with drive, curiosity, strong hands\-on data science and analytics experience (including forecasting, optimization, and simulation), and deep understanding of supply chain and operations. You know how to rise above the numbers and explain the crucial insights to users at all levels. You simplify and distill business complexity into testable hypotheses and scalable solutions. While you are proficient in various advanced modeling techniques, you are able to identify the technique optimal for the task at hand based on the business requirements, the available data, and the technique’s assumptions, interpretability, robustness, and limitations. You have a consulting mindset, ask good questions, are continually learning and keeping up with state\-of\-the\-art developments, and find opportunities to share knowledge with others.
WHAT YOU WILL WORK ON
You will be part of SCPT Data Science’s Sourcing, Supply, and Manufacturing (SSM) squad. As a Senior Data Scientist, you will build and deploy optimization and analytical models that power Nike’s global supply chain and support business strategies and initiatives spanning finished goods and materials sourcing, manufacturing, costing, and supply planning.
You will have opportunities to influence product direction, guide standard methodologies in data science and engineering, and help deliver technical projects from inception to completion. Specifically, you will:
- Design, develop, and productionize optimization and analytical models to support supply chain decisions. Apply statistical modeling and simulation techniques to complement optimization\-based solutions. Conduct model performance analysis, scenario testing, and sensitivity analysis to support decision\-making under uncertainty.
- Build and maintain end\-to\-end analytical pipelines, covering data ingestion, feature engineering, model training, validation, and deployment.
- Work with the squad as well as our global supply chain and operations partners to assess business requirements, data constraints, and pros and cons of alternative modeling techniques to determine the best solution approach and business tradeoffs. Contribute to the planning, scheduling and value measurement of the work to meet timeline targets and success criteria.
- Explore business drivers of cost, on\-time performance of products and materials, root causes of quality issues, and strive to achieve Nike’s sustainability goals.
- Help shape Nike’s analytical platforms and products by identifying foundational data science capabilities and creating reusable analytical components.
- Support the adoption of analytic products through effective storytelling and collaboration with key partners.
- Share knowledge with others on the team and contribute to best practices for model governance, reproducibility, and responsible use of data science solutions.
WHO YOU WILL WORK WITH
You will report to the Director of Data Science. As a Senior Data Scientist, you will collaborate closely with various global teams and stakeholders, including data science leads and peers, data engineers, software engineers, product owners and managers, and business end users to deliver cutting\-edge analytical and optimization solutions that shape how Nike plans and moves products and materials around the world.
WHAT YOU BRING
- Advanced quantitative degree (Statistics, Mathematics, Operations Research, Computer Science or related field) and at least 5 years of related industry experience as data scientist or applied scientist, or Bachelor’s degree and 7\-12 years related work experience. Will accept any suitable combination of education, experience or training.
- Deep knowledge of and hands\-on ability in data science and optimization methodologies, including classical models, artificial intelligence and machine learning algorithms, and linear and non\-linear optimization techniques. Hands\-on experience building optimization models using Python and commercial solvers (e.g., Gurobi, CPLEX, or equivalent).
- Advanced skills in programming languages (particularly Python and SQL) and ability to apply them for data acquisition, preprocessing, modeling, and monitoring.
- Familiarity with the wide range of data science/analytics software tools (e.g., Jupyter Notebook, SQL consoles, Hadoop, Spark) and cloud computing platforms (e.g., Amazon Web Services, Databricks).
- Experience in building, training, scoring, tuning and maintaining predictive models in production at enterprise scale and familiarity with mainstream packages relevant to managing all stages of the model lifecycle.
- Deep knowledge and experience in supply chain, supply planning, manufacturing \& sourcing, and logistics.
- Some hands\-on ability and knowledge in data engineering, software engineering, and at\-scale production
- Proven track record of working cross\-functionally and having a consulting mindset to critically evaluate complex business information from multiple perspectives, including questioning assumptions and validity.
- Familiarity with the Agile development process and demonstrable ability to prepare a project plan, communicate the plan to the team and break the work down to trackable tasks.
- Excellent written and verbal communication skills, including ability to develop and deliver presentation.
We are a highly agile group made up of small, integrated product, design, and engineering teams. Our teams take features all the way from the drawing board to the consumer, partnering closely and continuously across a wide variety of hardware, software, and services teams. If what we need doesn't exist, we make it. To succeed with us you should be open\-minded, thoughtful, collaborative and, above all, determined to produce great work for a global audience.
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 committed to employing a diverse workforce. 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.
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.
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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 4,317 AI roles we're tracking, Data Scientist positions make up 8% 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 $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
NIKE AI Hiring
NIKE has 1 open AI role right now. They're hiring across Data Scientist. Based in Beaverton, OR, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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