Job Title : Data Scientist AI, Generative AI & Predictive Analytics (Supply Chain Tech)

Experience : 3 - 6 Years

Location : Bangalore - hybrid

Industry : Supply Chain, Retail & E-commerce Technology

Employment Type : Full-Time

About the Role :

Join a fast-growing AI team at the forefront of reimagining Supply Chain Execution, OMS/WMS platforms, and software delivery through Machine Learning, Generative AI, and Intelligent Automation. As a Data Scientist, you will partner with product managers, supply chain SMEs, and engineering teams to turn complex, real-world business problems into AI-driven solutions spanning retail, e-commerce, and logistics ecosystems.

This role puts you at the intersection of applied ML, LLMs, AI agents, and predictive analytics, with direct ownership of outcomes that matter to global customers.

What You Will Own :

Modeling & Predictive Analytics :

- Build and fine-tune predictive models for demand forecasting, anomaly detection, fraud detection, and customer intelligence.

- Apply advanced analytics and optimization techniques to solve operational and decision-intelligence challenges unique to supply chain and retail.

- Evaluate and benchmark ML models using sound experimentation frameworks and performance metrics.

Generative AI & Intelligent Automation :

- Architect Generative AI solutions using LLMs, AI Agents, and Retrieval-Augmented Generation (RAG) pipelines.

- Translate unstructured business inputs into automated workflows and system configurations through intelligent automation.

- Build recommendation and personalization engines powered by customer behavior and contextual data.

Engineering & Deployment :

- Develop robust, scalable data pipelines and feature engineering workflows using Python and PySpark.

- Build production-grade APIs and services to operationalize AI models within enterprise SaaS platforms.

- Work cross-functionally with solution architects and engineers to embed AI capabilities into live products.

Collaboration & Communication :

- Contribute to AI platform architecture and reusable component design.

- Translate model outputs and insights into clear recommendations for both technical and business stakeholders.

- Stay ahead of emerging trends in Agentic AI, NLP, MLOps, and recommendation science.

What You Bring :

- 3 - 6 years in Data Science, ML, or AI roles, with strong production coding skills in Python and PySpark.

- Practical exposure to at least a few of : Classification/Regression/Clustering, Time Series Forecasting, Generative AI & LLMs, Recommendation Systems, NLP, or Optimization/Decision Intelligence.

- Working knowledge of Scikit-learn, Pandas, NumPy, TensorFlow, PyTorch, and Hugging Face.

- Proven experience deploying ML/AI solutions on cloud infrastructure.

- Solid grasp of the full ML lifecycle - data modeling, feature engineering, evaluation, and deployment.

- Comfortable working with Git in collaborative, version-controlled environments.

- Strong analytical and communication skills - able to explain "why" as clearly as "what."

Good to Have :

- Familiarity with agent frameworks like LangChain, LangGraph, or CrewAI.

- Exposure to RAG architectures, Vector Databases, and Knowledge Graphs.

- Hands-on with cloud ML platforms - AWS, Azure ML, or GCP Vertex AI.

- Experience with distributed processing tools like Spark/Hadoop.

- Understanding of MLOps fundamentals - CI/CD, versioning, monitoring.

- Prior exposure to OMS, WMS, Retail, or broader Supply Chain domains.

- Comfort operating in Agile teams.