Senior Data Scientist – Advanced Analytics & AI

Introduction

Your Role And Responsibilities

• Execute end-to-end Data Science projects including data collection, preprocessing, feature engineering, modelling, evaluation, and deployment.
• Design and implement advanced Machine Learning algorithms (classification, regression, clustering, ensemble methods) and Natural Language Processing algorithms (Knowledge Graphs, Topic Modelling, Feature Extraction, Sentiment Analysis, BERT etc.)
• Develop and deploy Generative AI and LLM-based solutions using platforms like OpenAI, Hugging Face, and LLama.
• Apply Agentic AI frameworks such as LangChain, LangGraph, Crew AI, or Microsoft Semantic Kernel to build intelligent applications.
• Proficient in designing and deploying scalable machine learning solutions using cloud architectures, with hands-on experience in at least one major platform (Azure, AWS, GCP, or IBM Cloud). Skilled in leveraging Databricks, ML platforms, managed databases, web hosting services, and document AI tools for end-to-end solution development.
• Collaborate with business stakeholders to define problem statements, deliver insights, and drive impact.
• Maintain reproducibility and version control using Git, GitHub
• Effectively communicate technical concepts and project outcomes to both technical and non-technical stakeholders.
  

Preferred Education

Required Technical And Professional Expertise

• 7–9 years of hands-on experience in Data Science, Machine Learning, Deep Learning, and NLP in a production environment.
• 2+ years of applied experience in Generative AI and LLMs (e.g., OpenAI, LLama).
• Proficiency in agentic AI development using frameworks like LangChain, LangGraph, or similar.
• Strong programming skills in Python and experience with ML/DL libraries
• Experience deploying models via REST APIs or web applications.
• Proficient in SQL for data extraction, transformation, and analysis.
• Experience working with large datasets, feature engineering, and data preprocessing pipelines.
• Solid understanding of model evaluation, cross-validation, and performance metrics.
• Experience in MLOps, including model testing, deployment pipelines, governance frameworks, and continuous monitoring for reliable and compliant machine learning operations.
• Experience with cloud ML pipelines and services (proficiency in at least one of Azure, AWS, GCP & IBM Cloud).
• Strong interpersonal and client communication skills.