Data Scientist

Faurecia; Pune District; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Faurecia is seeking an experienced Data Scientist/ML Engineer to join our AI team in Pune. You will design and implement production-grade ML solutions across manufacturing and product development domains.

The role requires 5+ years of hands-on experience in Python, ML/DL, NLP, and time-series modelling, with strong knowledge of PySpark, SQL, and data pipelines. Excellent communication and collaboration with global stakeholders are essential.

Qualifications

• 5+ years hands-on data science or ML engineering experience.
• Experience in industrial or manufacturing settings preferred.
• Proficient Python with production-grade pipelines.
• Solid ML algorithms knowledge (regression, clustering, boosting).
• Experience with DL, NLP, transformers, CV, time-series modelling.
• Familiarity with Generative AI, prompt engineering, RAG.
• Experience with ML/DL frameworks (sklearn, XGBoost, TF/Keras, PyTorch).
• Knowledge of SQL/NoSQL databases, data pipelines, MLOps basics.
• Cloud, Big Data platforms; Palantir Foundry a plus.
• Agile, DevOps, CI/CD practices.

Skills

Analytical mindset

Communication

Agile delivery

Problem solving

Stakeholder collaboration

Education

Engineering or related quantitative field

PhD preferred

Tools

Python

PySpark/Spark

SQL

Scikit-learn

TensorFlow/Keras

PyTorch

Hugging Face

LangChain/LlamaIndex

OpenCV

RAG/LLM tooling

Job description

Required qualifications and skills

• Engineering degree or Masters degree in Computer Science, Data Science, Artificial Intelligence, Applied Mathematics, Statistics, Engineering or a related quantitative field; PhD is a plus.
• Minimum 5 years of hands-on experience as a Data Scientist, Machine Learning Engineer, AI Engineer or equivalent role, ideally in industrial, automotive, manufacturing or product development environments.
• Strong experience in Python, with practical knowledge of PySpark/Spark, SQL, data manipulation libraries, APIs and software engineering practices for production-grade solutions.
• Solid understanding of machine learning algorithms such as regression, k-NN, SVM, Random Forests, gradient boosting, clustering and anomaly detection methods.
• Hands-on experience with deep learning, NLP, transformers, embeddings, information retrieval, computer vision, signal processing, forecasting and time-series modelling.
• Practical experience with Generative AI and LLM development, including prompt engineering, RAG, vector databases, LLM evaluation, hallucination mitigation and responsible AI principles.
• Experience with ML/DL frameworks and libraries such as Scikit-learn, XGBoost, TensorFlow, Keras, PyTorch, Hugging Face, LangChain/LlamaIndex, OpenCV or equivalent tools.
• Experience with SQL and NoSQL databases, data quality management, feature engineering, data documentation, model lifecycle management and scalable data pipelines.
• Knowledge of cloud, Big Data and enterprise data platforms; Palantir Foundry experience is a strong advantage.
• Knowledge of cost, product, BOM, industrial, manufacturing or finance data is a strong advantage.
• Knowledge of cloud, Big Data and enterprise data platforms; Palantir Foundry experience is a strong advantage.
• Good understanding of Agile delivery, DevOps, Git/version control, CI/CD, MLOps and LLMOps deployment principles.
• Strong analytical mindset, problem-solving capabilities, organizational skills and ability to manage priorities in a fast-moving international environment.
• Excellent communication, presentation and storytelling skills, with the ability to explain complex AI topics to technical and non-technical stakeholders.

Preferred experience

• Experience delivering AI or LLM solutions for engineering, manufacturing, quality, purchasing, sales, operations or product development use cases.
• Experience building enterprise assistants, knowledge search, document intelligence, agentic workflows or automation solutions using governed internal data.
• Exposure to cybersecurity, data privacy, intellectual property protection, access management and responsible AI governance in enterprise environments.
• Ability to define measurable business value, adoption KPIs and operational impact for AI solutions.