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
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Core Statistical & ML Modeling:
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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).
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Build, fine-tune, and evaluate classical machine learning algorithms (regression, classification, clustering, time-series forecasting, ensemble methods) for production systems.
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Generative AI & LLM Engineering:
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Design and implement Generative AI architectures, including Retrieval-Augmented Generation (RAG) pipelines, multi-agent frameworks, and vector search systems.
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Fine-tune, evaluate, and prompt-engineer open-source and proprietary Large Language Models (LLMs) for specific domain applications.
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Implement guardrails, evaluation frameworks (e.g., RAGAS, DeepEval), and toxicity/bias detection for LLM applications.
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Data Engineering & System Architecture:
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Collaborate with engineering teams to deploy ML/LLM models into production microservices via REST APIs, gRPC, or async worker queues.
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Optimize model inference latency, throughput, and GPU/memory consumption.
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Design data pipelines using Python, SQL, and modern data orchestration toolkits.
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Thought Leadership & Collaboration:
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Partner with product and business stakeholders to translate complex business problems into scalable machine learning and AI frameworks.
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Stay at the forefront of emerging research in machine learning, deep learning, and generative AI.
Required Qualifications & Technical Skills
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Education: Bachelor's, Master's, or Ph.D. in Computer Science, Statistics, Applied Mathematics, Data Science, Quantitative Finance, or a related quantitative field.
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Core Mathematics & Statistics:
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Strong expertise in probability distributions, stochastic processes, decision theory, regression analysis, and variance reduction techniques.
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Classical ML & Deep Learning:
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Proficiency with ML frameworks (scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow).
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Deep understanding of loss functions, optimization algorithms, evaluation metrics (ROC-AUC, RMSE, Precision/Recall, BLEU, ROUGE), and cross-validation techniques.
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Generative AI & LLM Ecosystem:
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Practical experience with LLM frameworks like LangChain, LangGraph, LlamaIndex, or DSPy.
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Hands-on experience with Vector Databases (ChromaDB, Pinecone, pgvector, Qdrant, or FAISS).
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Knowledge of quantization techniques (LoRA, QLoRA), embedding models, and multi-agent coordination frameworks (e.g., Model Context Protocol / MCP servers).
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Programming & Tools:
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High proficiency in Python (Pandas, NumPy, SciPy, PyTorch) and complex SQL.
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Familiarity with containerization (Docker) and modern development workflows (Git, CI/CD).
Preferred Qualifications
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Experience with time-series forecasting, quantitative modeling, or risk prediction algorithms.
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Knowledge of MLOps practices, model monitoring, and pipeline orchestration tools.
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Familiarity with cloud platforms (AWS, GCP, or Azure).