Urgent Hiring for Data Scientist with Agentic AI only with Consumer packaged goods (CPG) industry Experience.

Required skills & qualifications :

- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering or a related discipline.

- 3 to 12 years of experience in Data Science, AI, Machine Learning, GenAI or related areas.

- Strong Statistics and Machine Learning fundamentals : forecasting, prediction, optimization, model validation, largescale data analysis.

- Strong Python and SQL skills.

- Significant hands-on experience with Generative AI / LLMs (RAG, embeddings, prompt engineering, LLM evaluation).

- Demonstrated experience designing and building AI agents or agentic workflows - including agent orchestration, tool calling, agent memory/state, and agent evaluation. Experience limited to prompt engineering, basic LLM API integrations or simple chatbot development is not sufficient.

- Experience taking AI/ML solutions into production.

- Experience developing APIs or backend services (FastAPI or similar).

- Working knowledge of PostgreSQL or an equivalent relational database, and Git/version control.

- Hands-on experience with Docker and at least one cloud platform (AWS, Azure or GCP).

- Strong problem-solving and analytical ability, with the ability to independently own technical problems and drive them to completion.

Key Responsibilities :

- Architect and deploy Agentic AI workflows that automate complex decision-making processes, enabling the business to react in real-time to shifting consumer demand.

- Develop advanced forecasting and predictive models to optimize inventory levels and distribution logistics, ensuring high service levels while minimizing overhead costs.

- Implement robust model validation frameworks to ensure the reliability, fairness, and interpretability of AI-driven outputs across all consumer-facing applications.

- Partner with internal stakeholders to identify high-impact use cases for GenAI, transforming raw data into actionable insights that drive product innovation and marketing effectiveness.

- Lead the end-to-end lifecycle of machine learning projects, from initial hypothesis generation and data exploration to production deployment and continuous performance monitoring.