ML Engineer

ML Engineer - Demand Forecasting & Consumer Insights Remote with periodic travel to Costa Mesa CA What You Will Do Build or configure consumer insights models that synthesize real-time social media signals search trends and behavioral data into actionable design briefs - replacing the 9-month $200K research agency process with near-real-time intelligence Develop demand forecasting models at the SKU size and geography level to inform micro-batch production decisions - starting with 5000 units for the pilot and designed to scale Integrate data inputs from social platforms (TikTok Instagram Google Trends) and commercial trend tools (Heuritech WGSN Stylumia) into a unified signal layer Work with the AI Product Manager to define the data architecture for the pilot - lightweight enough to move fast in Phase 1 structured enough to scale in Phase 2 Validate model outputs against real sell-through data as the pilot progresses and iterate accordingly Document model logic and data pipelines so the approach is repeatable across categories and brands What You Need Production-level experience building demand forecasting or consumer demand sensing models - time-series forecasting regression or ML-based approaches; you have shipped models that drove real inventory or production decisions Hands-on experience with retail or consumer data - SKU-level sales data social listening data search trend data or equivalent; you understand how noisy and inconsistent this data is and how to work with it anyway Familiarity with at least one commercial forecasting or trend intelligence platform - o9 Solutions Blue Yonder Heuritech Stylumia or equivalent Strong Python skills and comfort working in cloud environments - models need to run in production and produce outputs the team can act on Ability to communicate model outputs in plain language to non-technical team members - the tiger team includes a designer and a marketing lead; you need to translate forecast confidence intervals into decisions they can make What You Do Not Need Experience in fashion or apparel - consumer behavior data is consumer behavior data; retail CPG or e-commerce forecasting experience is equally relevant Deep SAP or ERP integration experience - the pilot will use manual file transfers where needed; backend integration is Phase 2 A large team or large compute budget - the pilot is lean by design; the models need to work with limited data and limited infrastructure Nice to Have Experience with social commerce data pipelines - TikTok and Instagram engagement signals as demand proxies Familiarity with micro-batch production economics - unit economics of 50 500 unit test runs versus full production scaling Experience building consumer persona or segmentation models using LLMs Location & Commitment Primarily remote. Periodic travel to Costa Mesa CA for tiger team sprints. Full-time commitment for the duration of the pilot with expectation to continue into Phase 2 if the pilot succeeds.