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Tier-1 brand, mid-level ML role, metro location, and broad forecasting/ML skillset create strong competition.
Role requires specialized supply-chain forecasting and demand-planning domain knowledge, limiting cross-industry transferability.
Explicit 5–7 years, supply-chain forecasting expertise, and mandatory ML/cloud tooling make filters strict.
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Develop and deploy time-series forecasting and machine learning models to improve supply chain demand planning accuracy and responsiveness.
Lead end-to-end data science projects including data preparation, model building, productionization, and performance monitoring.
Collaborate with cross-functional and US-based teams to scale data-driven solutions and translate insights into actionable business recommendations.
5+ years of experience in data science, forecasting, or predictive analytics, preferably in Supply Chain, Telecom, Retail, or CPG.
Master's degree in data science, statistics, computer science, or a related field preferred (equivalent experience considered).
Proficiency in Python, SQL, and Excel with experience in machine learning frameworks and cloud ML platforms (e.g., AWS SageMaker, Azure ML, Databricks).
Experience with time-series forecasting models (ARIMA, Prophet, XGBoost) and scalable data science pipelines.
Experienced in large-scale forecasting systems and demand planning with measurable impact on forecast accuracy or KPIs.
Comfortable working autonomously and managing projects end-to-end with strong cross-functional collaboration skills.
Able to align and partner with US-based teams supporting US time zones for critical business functions.