TCS Virtual Drive - Data Scientist (PhD)

Greetings from Tata Consultancy Services !!!

TCS is hiring for Data Scientist (PhD)

Experience : 10 - 20 yrs

Location: Pan India

Required Skills:

10+ years of experience (5+ years in AI/Gen AI/Data Science is must).

Locations: PAN India, preferably Pune, Chennai, Hyderabad, Kolkata, NCR, Mumbai

Required Skills:

10+ years of experience (5+ years in AI/Gen AI/Data Science is must).

Locations: PAN India, preferably Pune, Chennai, Hyderabad, Kolkata, NCR, Mumbai

Key Responsibilities:

Design, develop, and deploy state-of-the-art ML and Generative AI models (e.g., LLMs, Transformers, Diffusion Models).
Conduct applied research in areas such as NLP, time-series, reinforcement learning, computer vision, or multimodal learning.
Collaborate with cross-functional teams including sales team, data engineers, agentic AI, Hyperscalers, MLOps, and product managers to build scalable solutions.
Contribute to and lead open-source initiatives and internal AI research related methods and different cutting-edge ML methodologies.
Prototype and evaluate algorithms with large-scale datasets .
Publish articles/blogs in internal and external forums (optional but encouraged).
Mentor junior researchers or data scientists.

Required Qualifications & Experience:

PhD in Machine Learning, Computer Science, Applied Mathematics, Statistics, Data Science, or a related field .
Strong foundation in ML/AI algorithms , probabilistic modeling, deep learning, and optimization.
3+ years of experience (post-PhD or during PhD/postdoc) developing production-grade ML/applied mathematics/Statistics/relevant field.
Proven experience with ML techniques like ANN, Non-Linear Regression, Stochastic process, Genetic Algorithm, advanced clustering, GB, RF, and Generative AI models: LLMs, GANs, VAEs, Diffusion, or Prompt Engineering.
Experience with large-scale datasets , merging, cleaning, feature selection, distributed training, and model optimization.

Technical Skills:

Programming : Expert in Python (NumPy, pandas, scikit-learn, PyTorch, TensorFlow, Hugging Face, etc.)
Frameworks & Tools : Experience with MLFlow, Ray, Docker, Git, FastAPI, Streamlit/Gradio
Cloud Platforms : Familiarity with AWS, GCP, or Azure ML services
MLOps : Understanding of CI/CD pipelines, model versioning, deployment (optional but preferred)
Strong skills in data wrangling, feature engineering , and experimentation

Preferred (Nice-to-Have):

Publications in top ML/AI conferences (NeurIPS, ICML, CVPR, ACL, etc.)
Contributions to open-source AI/ML libraries
Experience in building domain-specific LLMs or fine-tuning foundation models
Familiarity with prompt engineering, RAG architectures , or LLMOps pipelines
Experience with time-series forecasting, anomaly detection, or graph neural networks

Educational Background:

Data Scientist (PhD)