Nike Behavioral Interview Template for AI Engineer (Engineering)

What this covers: A structured, 60-minute behavioral interview focused on how you deliver athlete- and consumer-centric impact with AI at global scale, collaborate in a matrixed organization, and embody Nike’s values (often referenced internally as the Nike Maxims). The conversation emphasizes real outcomes on Nike digital experiences (e.g., personalization, demand/supply, experimentation), cross-functional teamwork with product, design, data/platform engineering, and privacy/legal partners, and Responsible AI practices. Format and flow: - Warm-up and context (5 minutes): Brief introductions, role scope, and how AI supports Nike’s consumer journey across regions (North America, EMEA, Greater China, APLA). - Behavioral deep dives (35 minutes): STAR-based prompts with follow-ups to uncover judgment, collaboration, and impact at scale. Core focus areas: 1) Athlete/consumer obsession: Examples where you translated insights into measurable outcomes (conversion, engagement, on-time delivery, latency/experience). Expect probing on metrics and trade-offs. 2) Team sport mindset in a matrix: Partnering across functions and time zones; handling conflict and ambiguity; influencing without authority; aligning with product launch timelines. 3) Responsible AI: Bias/fairness mitigation, privacy-by-design (especially for sensitive consumer data), model governance, and incident response. 4) Bias for action and learning agility: Rapid experimentation, A/B tests, iterating under changing priorities, and learning from failure. 5) Global mindset and cultural fluency: Delivering solutions that work across geographies, languages, and market nuances. - Scenario vignette (10 minutes): A Nike-relevant situation (e.g., scaling a personalization model ahead of a high-heat product drop) to assess prioritization, stakeholder alignment, risk management, and ethical considerations. - Values alignment (5 minutes): How your working style reflects Nike’s principles (e.g., serving athletes, winning as a team, doing the right thing). Be ready with concrete stories. - Candidate questions (5 minutes): Expect depth on collaboration patterns, model lifecycle, and how success is measured. Evaluation rubric (behavioral anchors): - Consumer impact: Clarity of problem framing, measurable outcomes, and ability to tie technical decisions to athlete/consumer value. - Collaboration in a matrix: Evidence of co-creation with cross-functional partners; healthy debate and resolution; influence without authority. - Responsible AI: Proactive risk identification; concrete fairness/privacy practices; thoughtful trade-offs under time pressure. - Learning agility and execution: Speed with quality; experiment design; retrospectives that inform next iterations. - Communication: Clear, structured narratives; quantification of impact; tailoring to technical and non-technical audiences. Red flags commonly cited: Over-indexing on tech without consumer outcomes; limited cross-functional partnering; dismissing privacy or fairness; difficulty operating in ambiguity or across time zones; vague metrics. Preparation guidance aligned to Nike style: Bring 5–6 STAR stories covering consumer impact, conflict resolution, a failed experiment and the learning, a fairness/privacy challenge, and a high-visibility launch. Quantify results (e.g., conversion lift, demand forecast accuracy, latency reduction). Be ready to explain trade-offs you made to protect the consumer experience during peak demand.