Senior Developer & AI Innovation Lead

Title: Senior Developer & AI Innovation Lead —Assortment Planning Location: Beaverton, OR Duration: X months contract 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, it’s about each person bringing skills and passion to a challenging and constantly evolving game. About the Role We are looking for a forward-thinking technologist who sits at the intersection of modern software development and applied AI. This is not a traditional developer role — it is a transformation role. You will inherit a production Assortment Planning that Sport Planners rely on daily to build multi-season demand forecasts, manage style-level assortment plans, and support the Line Planning process across Footwear, Apparel, and Equipment. Today, the tool is Excel and VBA-based. Tomorrow, it needs to be something fundamentally better. Your mission is to reimagine how planners interact with Assortment and Demand data — reducing cycle times from weeks to hours, replacing manual macro-driven workflows with intelligent, AI-augmented experiences, and making the platform resilient enough to evolve as business needs change. You will lead this evolution end-to-end: from vision through architecture through delivery. WHAT YOU WILL WORK ON Key Responsibilities AI Strategy & Transformation Define and execute the technical vision for modernizing the LRAP platform, moving from Excel/VBA to a scalable, cloud-native architecture Identify high-impact opportunities to apply AI/ML — such as forecast generation, anomaly detection in plan inputs, intelligent defaults, and natural-language plan adjustments — and prioritize them against business value Serve as the team's AI thought leader: evaluate emerging tools, frameworks, and paradigms (LLMs, copilots, agentic workflows) and translate them into practical capabilities for Sport Planning Platform Development & Architecture Design and build the next-generation LRAP platform, enabling planners to construct and adjust multi-season demand forecasts at Mid-Level through Style/Geo granularity Architect data pipelines that replace manual CSV ingestion with automated, real-time data flows from upstream systems Build intuitive interfaces that let planners filter, slice, and manipulate plans (by Sport, Gender, Category, Season, Silhouette, Geo) with the speed and flexibility they have today — without the fragility of spreadsheet macros Ensure seamless version control, import/export, and plan comparison capabilities that planners depend on for seasonal continuity Planner Partnership & Delivery Partner directly with Sport Planners to understand their workflows, pain points, and decision-making processes — turning planner feedback into rapid iterations Collapse the feedback-to-deployment cycle: drive a culture where plan adjustments and tool enhancements are delivered in hours, not weeks Own the end-to-end delivery lifecycle: requirements, design, development, testing, deployment, and ongoing support Maintain and support the existing LRAP tool during the transition period, ensuring zero disruption to active planning cycles Team Enablement & Influence Mentor teammates on AI-first development practices and modern engineering approaches Advocate for and demonstrate how AI can augment (not replace) planner expertise — building trust through practical, visible results Contribute to broader technology strategy by sharing learnings and patterns that can scale across the Planning organization WHAT YOU BRING Qualifications Required 5+ years of software development experience with strong proficiency in at least two of: Python, TypeScript/JavaScript, SQL, or cloud-native frameworks Demonstrated experience applying AI/ML in a product or business context — not just experimentation, but solutions that users depend on Proven ability to take a legacy tool or process and modernize it end-to-end (architecture, data layer, UX, deployment) Experience designing and building data-intensive applications — working with large, structured datasets, aggregation logic, and multi-dimensional planning grids Strong communication skills with the ability to translate between technical and business stakeholders; comfortable leading workshops, demos, and whiteboard sessions with non-technical planners Self-directed mindset: you can take ownership of a problem space, define the roadmap, and execute with minimal direction Strongly Preferred Working knowledge of demand planning, assortment planning, or merchandise financial planning processes — understanding how planners think about seasonal horizons, style-level forecasting, and geo-level allocation Experience with Excel/VBA automation tools and an appreciation for why users love spreadsheets (flexibility, speed, visibility) — and how to preserve those qualities in a modern platform Hands‑on experience with LLMs, prompt engineering, AI agents, or copilot‑style interfaces in an enterprise setting Familiarity with cloud data platforms (e.g., Snowflake, Databricks, BigQuery) and modern data orchestration tools Background in retail, consumer products, or supply chain technology Nice to Have Experience with forecasting models (statistical, ML-based) for demand or sales planning Familiarity with planning tools such as Anaplan, o9, Kinaxis, or similar Experience building tools that replaced or augmented spreadsheet-based workflows Background in sport or lifestyle retail What Success Looks Like In 30 days: You have deep fluency in the current LRAP tool, have built relationships with key planners, and have delivered a modernization roadmap with a prioritized backlog In 2 months: Planners are using at least one AI‑augmented capability that measurably reduces planning cycle time, and architecture for the next‑generation platform is in place In 3–6 months: The team has shifted off legacy Excel/VBA workflows for core use cases, plan adjustments happen in hours instead of weeks, and the platform is positioned to scale across additional planning horizons and teams #J-18808-Ljbffr