Data Scientist (PhD)

TCS Virtual Drive - Data Scientist (PhD)Greetings from Tata Consultancy Services !!!TCS is hiring for Data Scientist (PhD)Experience : 10 - 20 yrsLocation: Pan IndiaRequired Skills:10+ years of experience (5+ years in AI/Gen AI/Data Science is must).Locations: PAN India, preferably Pune, Chennai, Hyderabad, Kolkata, NCR, MumbaiRequired Skills:10+ years of experience (5+ years in AI/Gen AI/Data Science is must).Locations: PAN India, preferably Pune, Chennai, Hyderabad, Kolkata, NCR, MumbaiKey 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/GradioCloud Platforms: Familiarity with AWS, GCP, or Azure ML servicesMLOps: Understanding of CI/CD pipelines, model versioning, deployment (optional but preferred)Strong skills in data wrangling, feature engineering, and experimentationPreferred (Nice-to-Have):Publications in top ML/AI conferences (NeurIPS, ICML, CVPR, ACL, etc.)Contributions to open-source AI/ML librariesExperience in building domain-specific LLMs or fine-tuning foundation modelsFamiliarity with prompt engineering, RAG architectures, or LLMOps pipelinesExperience with time-series forecasting, anomaly detection, or graph neural networksEducational Background:Ph.D. in one of the following (or closely related) fields:Machine Learning / Artificial IntelligenceComputer Science / EngineeringApplied Statistics / MathematicsData Science / Analytics

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