Data Scientist

About the Role:

Key Responsibilities:

• Translate business challenges into solvable NLP and GenAI use cases, such as
  document understanding, web search, automated Q&A, summarisation, and workflow automation.
• Stay updated with the latest GenAI/LLM advancements and evaluate them for
  feasibility and potential use.
• Design, build, and deploy LLM-powered retrieval-augmented generation (RAG)
  pipelines and agentic AI solutions, including multi-step reasoning systems, tool-using agents, and associated pipelines.
• Build basic UI frontends (e.g., using Streamlit, Flask) for internal demos or
  client-facing pilot GenAI applications.
• Apply MLOps best practices, including MLflow-based tracking, Docker
  containerization, and CI/CD for GenAI pipelines.
• Develop customer demos and prototypes using Databricks MosaicAI suite.
• Contribute to both internal R&D efforts and customer implementations, including
  rapid POCs and scalable production deployments.

Required Qualifications:

• 4-8 years of implementation experience in machine learning, with a strong focus on NLP
  and GenAI applications in a customer-facing role.
• Must have productionized machine learning or deep learning models.
• Familiarity with SQL and working with large, complex datasets.
• Proficiency in Python and NLP/LLM libraries/tools such as HuggingFace Transformers,
  LangChain, LangGraph, LlamaIndex, etc.
• Practical experience with prompt engineering, chunking, vector embeddings, semantic
  search, RAG pipelines, and LLM fine-tuning.
• Understanding of GenAI-specific challenges - hallucination, prompt security, rate limits,
  cost optimisation, etc.

Strong foundation in statistics, including:

• Model assumptions and diagnostics
• Evaluation metrics and error analysis
• Probabilistic modelling, hypothesis testing, and uncertainty quantification
• Feature importance and interpretability techniques
• Experience in MLOps tools and processes, including:
• Model versioning and experiment tracking (e.g., MLflow)
• Containerization (Docker)
• CI/CD for ML workflows (e.g., GitHub Actions, Azure DevOps, or similar)
• Model monitoring and retraining workflows
  

Desirable:

Desirable:

Strong analytical and communication skills, with a demonstrated ability to convert business requirements into NLP/GenAI solutions. Educational Background Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, Statistics, Operational Research, or a related quantitative discipline. Relevant certifications (e.g., Databricks certifications, AWS/Azure/GCP AI/ML certifications) are a plus.