Data Scientist

We are seeking a Data Scientist who is passionate about transforming raw data into intelligent business insights and building models that power real-world decision-making. This role involves working across the entire data science lifecycle- from data exploration and modelling to GenAI integration and insight generation. You will play a key role in designing, developing, and deploying data science solutions across various domains, including healthcare, finance, and enterprise analytics. You will collaborate closely with cross-functional teams, including frontend developers, designers, product managers, and AI/ML specialists, to deliver high-performance, AI-integrated digital solutions. This is an excellent opportunity for someone passionate about clean architecture, performance optimisation, and building intelligent, user-centric applications.

The core responsibilities for the job include the following:

Machine Learning and Predictive Modelling:

• Build and deploy ML models for tasks like classification, regression, clustering, recommendation, etc.
• Feature engineering and model tuning for optimal performance and accuracy.
• Conduct A/B testing and model validation using industry best practices.

Generative AI and NLP:

• Work with LLMs and GenAI frameworks (e. g., OpenAI, HuggingFace, LangChain) to design applications like intelligent report generators, summarizers, Q& A bots, etc.
• Apply NLP techniques for entity recognition, sentiment analysis, topic modelling, semantic search, etc.
• Experiment with RAG (retrieval-augmented generation), prompt engineering, and fine-tuning foundation models.

Model Deployment and MLOps:

• Deploy models using MLOps pipelines (MLflow, SageMaker, Bedrock, Vertex AI, etc. ).
• Monitor performance, retrain models, and ensure their scalability, security, and maintainability.
• Work with APIs and cloud infrastructure to integrate models into production systems.

Requirements:

• 7+ years of experience as a Data Scientist or ML Engineer.
• Strong expertise in Python and libraries like Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch.
• Experience in end-to-end ML lifecycle, including model development, validation, and deployment.
• Solid understanding of statistics, probability, and data modelling.
• Experience with NLP libraries (NLTK, spaCy, Hugging Face Transformers).
• Hands-on experience with GenAI tools: OpenAI APIs, LangChain, RAG, LLM fine-tuning.
• Experience with SQL and at least one NoSQL database.
• Familiarity with cloud platforms (AWS, GCP, Azure) and version control (Git).
• Strong communication skills with the ability to explain complex models to non technical Audiance.