Data Scientist

Job Overview

The Data Scientist is responsible for leading advanced data science, deep learning, and Generative AI initiatives to solve complex business problems. The role involves designing scalable ML and AI systems, building LLM-powered applications and autonomous agents, and translating advanced analytics into strategic business outcomes. The position requires strong expertise across machine learning, deep learning, Generative AI, big data platforms, and MLOps, with the ability to influence senior stakeholders.

Key Responsibilities

       Lead end-to-end Data Science, Deep Learning, and Generative AI projects from ideation to production.

       Design, build, and deploy machine learning, deep learning, and AI-driven solutions at scale.

       Develop and optimize Deep Learning models using TensorFlow, PyTorch, and Keras (CNNs, RNNs, LSTMs, Transformers).

       Build Generative AI applications using LLMs (OpenAI, Azure OpenAI, Hugging Face, Anthropic) for use cases such as Q&A, summarization, recommendations, and content generation.

       Design and implement Agentic AI systems (LLM agents) using LangChain, LangGraph, AutoGen, or custom frameworks for task orchestration, tool usage, and decision-making.

       Implement Retrieval-Augmented Generation (RAG) pipelines using vector databases (FAISS, Pinecone, Azure AI Search, Chroma).

       Perform advanced feature engineering, EDA, statistical analysis, and hypothesis testing.

       Build scalable data and ML pipelines using Azure Databricks / AWS Databricks, Spark, and Delta Lake.

       Deploy, monitor, and retrain models using MLOps practices (MLflow, CI/CD, model versioning, drift detection).

       Collaborate with data engineers, product teams, and business stakeholders to align AI solutions with business goals.

       Translate complex AI/ML results into clear, actionable insights for leadership and non-technical audiences.

       Mentor junior data scientists and guide teams on best practices in ML, Deep Learning, and GenAI.

Required Skills

       Strong expertise in Machine Learning, Deep Learning, and Generative AI.

       Hands-on experience with Python and ML/DL libraries: Scikit-learn, TensorFlow, PyTorch, Keras, XGBoost.

       Experience building and fine-tuning LLMs and transformer-based models.

       Strong experience with LLM frameworks: LangChain, LangGraph, AutoGen, LlamaIndex.

       Experience with Prompt Engineering, RAG architecture, and AI Agents.

       Hands-on experience with Azure Databricks / AWS Databricks, Spark, Delta Lake.

       Strong SQL skills and experience with large-scale data warehouses and data lakes.

       Experience with Azure ML, AWS SageMaker, and ML lifecycle management.

       Expertise in MLOps tools: MLflow, CI/CD pipelines, Docker, Kubernetes.

       Experience with vector databases and embeddings for semantic search and GenAI use cases.

Preferred Skills

       Experience in Data Science, Applied ML, Deep Learning, or AI Engineering.

       Strong experience with cloud platforms (Azure and/or AWS).

       Hands-on experience with big data and distributed systems (Spark, Kafka, Airflow).

       Experience designing production-grade GenAI and agent-based systems.

       Strong understanding of statistics, probability, optimization, and experimentation.

       Experience with NLP, Computer Vision, Time Series Forecasting, and Recommendation Systems.

       Ability to simplify complex AI concepts into clear business value propositions.

       Proven leadership, ownership, and mentoring capabilities.

       Comfortable working in fast-paced, ambiguous, and innovation-driven environments.