ML Engineer - Apparel & footwear Domain ( Must )

Sira Consulting is an Inc 5000 company focused on building an AI-native product creation pipeline for a global apparel and footwear company. The ML Engineer will be responsible for developing models that forecast consumer demand and integrate real-time data signals to inform production decisions.

Responsibilities:

• 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 5,000 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

Requirements:

• 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
• 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