Data Scientist 2

We are seeking an innovative and driven Data Scientist to join our dynamic team. This role is at the heart of our mission to build intelligent systems that solve real-world business problems. You will be instrumental in designing and deploying cutting-edge solutions that leverage Generative AI, agentic systems, deep learning, and classical machine learning. The ideal candidate is a hands-on builder with a strong foundation in data analysis and software engineering, who thrives in a fast-paced environment and is passionate about pushing the boundaries of what's possible with AI. Experience in the pharmaceutical or life sciences domain is highly valued.Key ResponsibilitiesDesign & 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.

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.). Data Analysis & Machine Learning: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. Traditional NLP Expertise: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