Senior Data Scientist

Providence Global Center; Hyderabad; Hybrid; INR 1,500,000 - 3,000,000; Full time

Job summary

The role requires deep foundations in probability, statistical inference, and ML, plus hands-on experience with LLMs, vector databases, and deploying models in production environments.

Qualifications

• Strong foundation in probability, statistics, and ML theory.
• Experience building end-to-end data science and AI systems.
• Proficiency with Python, SQL, and ML frameworks.

Responsibilities

• Develop and deploy end-to-end data science solutions.
• Build and tune ML models and RAG pipelines.
• Collaborate with engineering for production deployment.
• Ensure model monitoring and evaluation.

Skills

Probability theory

Statistics

Bayesian methods

Hypothesis testing

A/B testing

Python

SQL

ML frameworks

Education

Quantitative degree (CS/Stats/Math)

Tools

scikit-learn

XGBoost

LightGBM

PyTorch

TensorFlow

Job description

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).