Data Scientist 2

Location: Bengaluru, India

Department: Projects & Delivery

Experience: 3-4 years

Skills: Ai, Data Science, LLM, Machine Learning

Key Responsibilities

• Design & Build GenAI Systems: Lead the design, development, and deployment of robust Generative AI solutions, including agentic systems and Retrieval-Augmented Generation (RAG) pipelines to tackle complex business challenges.
• Develop Intelligent Agents: Create sophisticated agents capable of reasoning and executing tasks over large-scale structured data (e.g., databases, APIs) and unstructured data.
• Ensure System Reliability: Establish and implement rigorous frameworks for evaluating, testing, and ensuring the reliability, safety, and accuracy of LLM-based systems.
• End-to-End Model Ownership: Apply a wide range of machine learning and deep learning techniques (e.g., forecasting, CLV, recommendation systems, NLP) and own the entire model lifecycle—from rapid prototyping to deploying scalable, low-latency solutions using Docker and AWS services.
• Large-Scale Data Mastery: Process and analyze massive datasets (billions of records) using distributed computing frameworks like PySpark to extract actionable insights and engineer impactful features.
• Experimentation & Communication: Design and conduct experiments to validate hypotheses, perform insightful EDA, and effectively communicate solution outlines and results to stakeholders and team members.
• Mentorship & Collaboration: Mentor junior team members and act as a bridge between business problems and data science, working closely with cross-functional engineering and product teams.

Core Qualifications & Skills

• Experience: 3-4 years of hands-on experience in a data science role, building and deploying machine learning models in a production environment.
• Generative AI Proficiency: Demonstrated experience building solutions using Large Language Models (LLMs), with specific expertise in RAG architectures and agentic frameworks (e.g., LangChain, Langgraph, or similar).
• ML & Deep Learning: Strong foundation in classical machine learning algorithms and deep learning architectures (ANN, CNNs, LSTMs, Transformers).
• Programming & Data Analysis: High proficiency in Python and its data science ecosystem (Pandas, NumPy, Scikit-learn). Excellent SQL skills are a must.
• Big Data Technologies: Proven experience with distributed data processing frameworks, particularly PySpark.
• Problem-Solving: Exceptional analytical, logical reasoning, and problem-solving skills with a data-driven approach.
• Engineering & Deployment: Solid understanding of system design concepts and MLOps principles, including containerization (Docker) and cloud services (AWS stack: S3, Lambda, ECR, Step Functions, etc.).

• Utilize Python libraries like NumPy, Pandas, and Dask, Pyspark for data processing and analysis.
• Apply ML/DL libraries like Scikit-learn, TensorFlow/Keras, PyTorch for developing and deploying models.

• Work on advanced NLP techniques including Transformers (BERT, T5, GPT), Word2Vec, NER, topic modeling, and contrastive learning.
• Good understanding of Python Ecosystem and implementing research papers 

• Work closely with cross-functional teams and clients to deliver impactful solutions.
• Fast paced development and Rapid prototyping environment.

• Domain expertise in Pharma or Life Sciences, with an understanding of claims data, commercial analytics.
• Hands-on experience with fine-tuning open-source LLMs (e.g., Llama, Mistral) for specific tasks.
• Deep understanding of the latest developments in agentic systems, including MCP and multi-agent frameworks (e.g., AutoGen, CrewAI).
• Experience with advanced ML techniques such as Positive-Unlabeled (PU) learning, representation learning, and advanced model explainability.
• Prior experience in building domain-specific models like Marketing Mix Models (MMM), demand forecasting, or multi-dimensional time series analysis.
• Contributions to open-source AI/ML projects or publications in relevant fields.