We are seeking a highly skilled Senior Machine Learning Architect / Senior ML Individual Contributor to design and build a production-grade, repeatable system for optimal store–SKU model stock distribution for a national brick‑and‑mortar retailer (1,200+ stores, ~300 SKUs).
In this role, you will break the limitations of current tier‑based forecasting by architecting a machine‑learning‑driven optimization framework that captures rich, multi‑dimensional signals to localize demand and improve SKU‑store alignment.
You will own the full lifecycle—from translating business objectives into measurable optimization targets, to deploying containerized solutions in production, to validating incremental lift through experiments. You will operate with architectural authority, functioning as a senior IC who can turn whiteboard concepts into fully deployed systems that merchandising and supply‑chain teams trust.
Responsibilities
Core Responsibilities
- Architect end‑to‑end machine learning systems for high‑dimensional retail demand modeling.
- Design and build production‑grade Python ML pipelines that retrain and adapt automatically.
- Develop and test advanced forecasting approaches including hierarchical forecasting, gradient boosting, time‑series models, and hybrid ML techniques.
- Engineer complex feature pipelines integrating regional, demographic, climate, historical sell‑through, promotional, and behavioral signals.
- Design allocation or constrained optimization logic to convert demand forecasts into store‑SKU model stock targets.
- Validate model performance through backtesting, live pilots, and controlled experiments demonstrating measurable lift.
- Deploy solutions in containerized production environments.
- Work cross‑functionally to translate ambiguous business problems into measurable outputs aligned with stockout reduction, model‑stock accuracy, and sales lift.
- Document methodology clearly and rigorously for leadership and cross‑functional stakeholders.
Success Indicators
- Forecast‑based initial stock models that materially outperform current baselines.
- A robust, repeatable pipeline that recalibrates and adapts seamlessly.
- Quantifiable reduction in stockouts and improved sell‑through.
- A trusted system used by merchandising and supply‑chain teams.
Requirements
Required Experience
- 7+ years in applied machine learning, data science, or related fields.
- Proven experience building multi‑dimensional forecasting systems (e.g., store‑SKU, geo‑segmented).
- Strong background in demand forecasting, inventory optimization, or retail analytics.
- Production deployment experience in containerized environments.
- Deep proficiency in Python and modern ML frameworks.
- Experience working with large, structured datasets at scale.
- Demonstrated ability to take solutions from whiteboard to production.
Strongly Preferred
- Retail or supply‑chain domain expertise.
- Experience with hierarchical time‑series forecasting.
- Experience building allocation or optimization layers atop forecasting models.
- Familiarity with experimentation frameworks (A/B testing, pilot rollouts).
- Background in consulting or startup environments with high ambiguity and fast iteration.
Work Timing
US Central Standard Time, Remote from anywhere in India
Hours per week
40
Duration of Contract
120 Days
Application Process
Please visit Jobs at Mindverse Consulting Services Private Limited and locate the applicable job title. Read the JD in details and Click “I’m Interested” and fill out accordingly.
Attach your CV and any other document you deem fit along with the “Screening Questions” which is mandatory. Please be specific and detailed. Note these questions are from the custom to evaluate your candidature.
Any vague or inconclusive answer will not be considered.
Incase of any query, feel free to reach out to
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