You will design, develop, and deploy end‑to‑end analytics, machine learning, and GenAI solutions , working with large‑scale structured and unstructured data. The role balances classical ML and statistical modeling with modern LLM‑based architectures , ensuring both approaches are applied where they create the most value.

Apply machine learning techniques such as regression, classification, clustering, ensemble methods, deep learning, and time‑series analysis

Develop scalable analytics pipelines and reusable modelling frameworks suitable for automation and production deployment

Design and implement Generative AI and LLM‑based solutions , including NLP, NLG, and semantic search

Build and deploy Retrieval Augmented Generation (RAG) pipelines for enterprise knowledge and data extraction use cases

Fine‑tune, evaluate, and monitor Large Language Models for business applications

Apply text preprocessing, tokenization, embedding generation, prompt engineering, and LLM orchestration techniques

Transformers, LangChain‑based workflows) to build scalable AI applications.

Build end‑to‑end system designs covering data ingestion, feature engineering, modeling, inference, and user‑facing outputs

Partner with IT and platform teams to deploy analytics and AI solutions on cloud infrastructure

Explore new data sources, tools,



and technologies to continuously enhance analytics and AI capabilities

Package findings into explicit documentation, dashboards, and executive‑level presentations

Bachelor's or Master's degree in Data Science, Computer Science, Engineering, Statistics, Applied Mathematics, Operations Research, or related quantitative field

- Robust hands‑on experience in Python for data analysis, machine learning, and AI development
- Experience with common ML and analytics libraries (e.g., Experience with SQL and working with relational and large‑scale datasets
- Experience building analytical models and communicating insights to business stakeholders
- 5+ years of experience applying machine learning, advanced analytics, and AI techniques in real‑world settings
- Strong experience with Generative AI, LLMs, NLP, and RAG architectures
- Experience with cloud platforms, especially Google Cloud Platform (GCP) , including Vertex AI, BigQuery, and related services
- Experience working with large‑scale data ecosystems (e.g., NoSQL databases, Hadoop ecosystem)
- Strong mathematical foundations in probability, linear algebra, optimization, and numerical methods
- Excellent problem‑solving, communication, and data storytelling skills

📌 Data Scientist (Python, ML, SQL) (Chennai)
🏢 Ford Motor
📍 Chennai