Data Scientist
Summary
Build and deploy AI models for CPG demand forecasting, supply-chain optimization, and generative-AI agents using Python and cloud MLOps.
Senior Data Scientist – Key Requirements
- 5–8 years of hands-on experience in data science, with a strong track record of delivering production-grade solutions in business environments.
- Deep expertise in traditional machine learning techniques, including time series forecasting (e.g., ARIMA, SARIMA), regression models, and other statistical methods applied to real-world datasets.
- Proven experience designing and implementing optimization models (e.g., linear programming, mixed-integer programming, heuristics) to solve complex business problems such as demand planning, inventory optimization, or resource allocation.
- Recent, demonstrable experience working on Generative AI initiatives, including LLM-based solutions and agentic workflows (e.g., autonomous agents, multi-step reasoning pipelines, tool-augmented AI systems).
- Hands-on experience in designing, deploying, and operationalizing agentic workflows in production environments, including orchestration, tool integration, monitoring, and performance optimization of AI-driven agents.
- Strong domain knowledge in Consumer-Packaged Goods (CPG), with hands-on experience in at least one functional area: Sales (e.g., demand forecasting, pricing), Finance (e.g., revenue/margin analytics), or Supply Chain (e.g., planning, logistics, inventory).
- Ability to translate business problems into scalable analytical and AI-driven solutions, partnering closely with cross-functional stakeholders such as product managers, engineers, and business teams.
- 100% hands-on role with active involvement in data exploration, model development, evaluation, deployment, and continuous improvement.
- Proficiency in Python and relevant data science ecosystems (e.g., pandas, scikit-learn, statsmodels, PyTorch/TensorFlow), along with experience in cloud platforms and MLOps practices.