Job Description: Senior Data Scientist (Statistics, ML & Generative AI) Location: Hyderabad, Telangana, India / Hybrid Role Type: Full-Time Position Overview We are looking for a highly analytical and technical Senior Data Scientist with a strong mathematical foundation in statistics, probability, and classical machine learning, paired with cutting-edge experience in Large Language Models (LLMs) and Generative AI. In this role, you will bridge the gap between rigorous statistical modeling and state-of-the-art AI technologies. You will design, develop, and deploy end-to-end data science solutions, RAG (Retrieval-Augmented Generation) architectures, and predictive models that turn complex data into strategic business value. Key Responsibilities • Core Statistical & ML Modeling: • Apply advanced probability theory, hypothesis testing, Bayesian methods, and statistical inference to validate data distributions, perform predictive analytics, and design robust experiments (A/B testing). • Build, fine-tune, and evaluate classical machine learning algorithms (regression, classification, clustering, time-series forecasting, ensemble methods) for production systems. • Generative AI & LLM Engineering: • Design and implement Generative AI architectures, including Retrieval-Augmented Generation (RAG) pipelines, multi-agent frameworks, and vector search systems. • Fine-tune, evaluate, and prompt-engineer open-source and proprietary Large Language Models (LLMs) for specific domain applications. • Implement guardrails, evaluation frameworks (e.g., RAGAS, DeepEval), and toxicity/bias detection for LLM applications. • Data Engineering & System Architecture: • Collaborate with engineering teams to deploy ML/LLM models into production microservices via REST APIs, gRPC, or async worker queues. • Optimize model inference latency, throughput, and GPU/memory consumption. • Design data pipelines using Python, SQL, and modern data orchestration toolkits. • Thought Leadership & Collaboration: • Partner with product and business stakeholders to translate complex business problems into scalable machine learning and AI frameworks. • Stay at the forefront of emerging research in machine learning, deep learning, and generative AI. Required Qualifications & Technical Skills • Education: Bachelor's, Master's, or Ph.D. in Computer Science, Statistics, Applied Mathematics, Data Science, Quantitative Finance, or a related quantitative field. • Core Mathematics & Statistics: • Strong expertise in probability distributions, stochastic processes, decision theory, regression analysis, and variance reduction techniques. • Classical ML & Deep Learning: • Proficiency with ML frameworks (scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow). • Deep understanding of loss functions, optimization algorithms, evaluation metrics (ROC-AUC, RMSE, Precision/Recall, BLEU, ROUGE), and cross-validation techniques. • Generative AI & LLM Ecosystem: • Practical experience with LLM frameworks like LangChain, LangGraph, LlamaIndex, or DSPy. • Hands-on experience with Vector Databases (ChromaDB, Pinecone, pgvector, Qdrant, or FAISS). • Knowledge of quantization techniques (LoRA, QLoRA), embedding models, and multi-agent coordination frameworks (e.g., Model Context Protocol / MCP servers). • Programming & Tools: • High proficiency in Python (Pandas, NumPy, SciPy, PyTorch) and complex SQL. • Familiarity with containerization (Docker) and modern development workflows (Git, CI/CD). Preferred Qualifications • Experience with time-series forecasting, quantitative modeling, or risk prediction algorithms. • Knowledge of MLOps practices, model monitoring, and pipeline orchestration tools. • Familiarity with cloud platforms (AWS, GCP, or Azure).