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Data Scientist at Ford Global Career Site
Chennai, tamil nadu, India -
Full Time

Start Date

Immediate

Expiry Date

08 Jun, 26

Salary

0.0

Posted On

10 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Machine Learning, Generative AI, LLMs, Python, SQL, Deep Learning, NLP, RAG, Prompt Engineering, TensorFlow, PyTorch, LangChain, Transformers, Data Analysis, Model Validation, Cloud Platforms

Industry

Motor Vehicle Manufacturing

Description
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. * Translate business objectives into well‑defined analytical and mathematical problem statements * Design, implement, train, tune, validate, and monitor predictive and prescriptive models * Apply machine learning techniques such as regression, classification, clustering, ensemble methods, deep learning, and time‑series analysis * Analyze large, high‑dimensional datasets to identify patterns, drivers, anomalies, and trends * 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 * Leverage modern GenAI frameworks and libraries (e.g., Transformers, LangChain‑based workflows) to build scalable AI applications.
Responsibilities
The role involves designing, developing, and deploying end-to-end analytics, machine learning, and Generative AI solutions, balancing classical ML with modern LLM-based architectures to solve complex business problems. Responsibilities include translating business needs into analytical problems, implementing and monitoring predictive models, and building scalable Generative AI solutions like RAG pipelines.