Job Title: Senior Developer & AI Innovation Lead Assortment Planning
Location: Beaverton, OR
Duration: 06 months
Hybrid: Mon Thurs onsite, Fri remote
Job Description
Location and/or time zone: Onsite at PHK with fridays WFH
Reason for opening: Project Support
Expected contract length: 26 W, 1 D
Team details & who they will work with: Global Sports Planning
Must Have Skills
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
Desired soft skills:
Minimum required years of experience
5 years of software development experience with strong proficiency in at least two of: Python, TypeScript/JavaScript, SQL, or cloud-native frameworks
Education requirements: Bachelors not required
Soft skills:
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
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.
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
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