Abhishek Datta

About Abhishek Datta

AI scientist and data science leader with 20+ years of global experience designing, training, and deploying machine learning and AI systems across banking, insurance, retail/CPG, telecom, oil & gas, mining, manufacturing, and healthcare- Proven track record of building end‑to‑end AI solutions (ASR, OCR, computer vision, NLP, forecasting, pricing/recommendation) and leading teams from research and prototyping to production at enterprise scale- Strong foundation in applied mathematics and statistics (dual post‑graduate degrees, peer‑reviewed machine learning publication) with deep hands-on experience in deep learning, language models, and GenAI, combined with pre sales, client acquisition, and stakeholder leadership- Core AI & Research Skills: Machine learning & statistics: supervised/unsupervised learning, ensemble methods, time‑series forecasting, probabilistic modeling, feature engineering, model evaluation and validation- Deep learning: CNNs, RNNs/LSTMs, transformers, representation learning, transfer learning, multimodal models- Generative AI & LLMs: large language models, prompt engineering, RAG pipelines, instruction tuning, domain adaptation, LLM‑based agents and tools integration- Natural language processing: text classification, sentiment analysis, topic modeling, information extraction, document intelligence, semantic search- Computer vision: object detection and tracking, face recognition, OCR, image classification and segmentation- MLOps & LLMOps: experiment tracking, CI/CD for ML, model deployment and monitoring, Kubernetes‑based microservices, data & model versioning, observability for ML/LLM systems- Data platforms & big data: data pipelines, SQL/NoSQL, distributed processing (Hadoop, Spark, Hive), feature stores and data governance concepts- AI governance & ethics: bias and drift awareness, interpretability, risk‑aware deployment and monitoring for regulated industries (banking, insurance, healthcare)- Technical StackProgramming & analysis: Python, R, SQL, MATLAB, C, SAS, SPSS- ML / DL frameworks: PyTorch, TensorFlow, Keras, scikit‑learn, pandas, NumPy- GenAI / LLM ecosystem: Hugging Face Transformers, LangChain / LangGraph (or similar orchestration), vLLM/Ollama‑style inference stacks, prompt/version management tools- Cloud & MLOps: Azure ML, AWS SageMaker, Kubernetes, Docker, MLflow‑style experiment tracking, CI/CD for ML- Data & BI: Hadoop, Pig, Spark, Hive, Tableau, Power BI, SAS Enterprise Miner, RapidMiner, SPSS Clementine, Azure ML Studio, IBM Watson.

Experience

1. Derivatives Trader

   Self

   Jul 2020 — Present · Kolkata, IN

   Current focus on building and deploying AI- based strategies for Options and Crypto trading

Education

• Stony Brook University

  Master of Science - MS, Applied Mathematics & Statistics

• Stony Brook University

  Master's degree, Applied statistics

• Stony Brook University

  Master's degree, Mathematics and Statistics

• Indian Institute of Technology, Kharagpur

  Integrated M.Sc (B.Sc+M.Sc), Mathematics

  1995 — 2000