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Recognizable global brand, metro location, and mid-senior technical role produce medium applicant competition.
Role's deep retail/CPG forecasting and supply-chain optimization focus creates high industry-specific background sensitivity.
Explicit 8+ years, advanced degree, and specialized GenAI/time-series stack create high shortlisting strictness.
Lead and mentor a small team of data scientists to deliver high-quality, production-ready predictive and prescriptive analytics for retail and CPG enterprise clients.
Own and manage key enterprise client data science engagements, translating complex business problems into scalable data science solutions and communicating ROI to executive stakeholders.
Drive adoption and innovation of GenAI, LLMs, and Agentic AI to improve delivery speed and scalability of professional services and integrate algorithmic advances into SaaS product features.
8+ years of progressive data science experience including 1–2 years of technical team mentorship or management.
Master’s or Ph.D. degree (or equivalent experience) in Data Science, Computer Science, Operations Research, Statistics, Industrial Engineering, Applied Mathematics, or related quantitative field.
Expertise in time series forecasting, machine learning, deep learning, Python, SQL, data science libraries, and cloud lakehouse platforms (Databricks, Azure).
Working knowledge of GenAI technologies, prompt engineering, multi-agent frameworks (e.g., LangChain/AutoGen), and experience with customer-facing analytics delivery.
Experienced in leading small technical teams focused on enterprise SaaS analytics and professional services in retail or CPG supply chain contexts.
Strong operational skills in applying advanced time series and optimization models combined with deep technical proficiency in cloud data platforms and GenAI applications.
Capable of strategic client engagement including scoping, managing expectations, and translating technical solutions into clear business value for senior stakeholders.