Data Scientist

The core requirements for the job include the following:
Core Programming and Tools:
Hands-on experience building and deploying ML models on large-scale production datasets.
Strong programming proficiency in Python with production-quality, well-structured code.
Proficient in SQL; experience working with large-volume datasets in Spark and/or Snowflake.
Experience with Git for version control, code review, and collaboration.
Familiarity with experiment tracking and model versioning (e. g., MLflow).
Classical ML and Statistical Modelling:
Time series forecasting (ARIMA, Prophet, deep learning approaches).
Optimisation modelling (linear programming, mixed-integer programming).
Classification, regression, clustering, and ensemble methods.
Feature engineering, data wrangling, and statistical analysis on structured data.
LLM Application and AI Agent Development:
AI agent development: experience building agentic frameworks using Azure AI Studio and Azure model endpoints, including tool-use patterns and multi-step orchestration.
RAG pipeline development: document ingestion/chunking, vector embedding/indexing (Azure AI Search), and response generation using LLM endpoints.
Prompt engineering and chain-of-thought design for complex supply chain workflows.
Experience with orchestration frameworks like LangChain or LlamaIndex.
Azure and Production Delivery:
Familiar with core Azure resources: Azure Machine Learning, Azure AI Studio, Azure OpenAI Service, and Azure storage/compute.
Experience delivering production-ready models with proper testing, validation, and monitoring.
Basic UI skills (Streamlit or Gradio) for quick prototyping and demos.
Work Style:
Highly self-motivated, collaborative, and able to deliver in a fast-paced environment.
Able to start contributing within 1-2 weeks with minimal ramp-up.
Nice to Have:
Supply chain domain experience (demand forecasting, inventory optimisation, logistics, etc. )
Familiarity with ERP data structures (Oracle, SAP).

Experience
7-11 yrs