Senior Data Scientist

We are seeking an experienced and hands-on Senior Data Scientist with expertise in Generative AI and/or Classical Machine Learning to join our growing AI team. In this role, you will lead the design and development of data-driven solutions, ranging from traditional ML models to cutting-edge GenAI applications that solve real business problems.

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  Develop, validate, and deploy ML models for prediction, classification, recommendation, and other business use cases.

  
  


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  Design and build GenAI applications using LLMs (e.g., GPT, LLaMA, Claude) for use cases like summarization, Q&A, content generation, document analysis, etc.

  
  


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  Collaborate with stakeholders to translate business problems into data science solutions.

  
  


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  Perform EDA, feature engineering, and data preprocessing on structured and unstructured data (text, images, documents).

  
  


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  Implement model performance monitoring, drift detection, and retraining strategies.

  
  


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  Work with MLOps/engineering teams to ensure scalable, production-grade model deployment.

  
  


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  Stay up to date with new GenAI and ML research trends and identify opportunities for business impact.

  
  


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  Mentor junior data scientists and support peer review of code, model design, and documentation.

  
  

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  Education: Master’s or Bachelor's degree in Computer Science, Data Science, Statistics, or related field.

  
  


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  Experience: 5–8 years of professional experience in data science, with proven delivery of ML/AI models in production.

  
  

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  Strong programming skills in Python and use of libraries such as Pandas, Scikit-learn, NumPy, TensorFlow, PyTorch.

  
  


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  Experience building and fine-tuning ML models for regression, classification, clustering, or time-series forecasting.

  
  


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  Hands-on experience with LLMs and GenAI frameworks (e.g., LangChain, Hugging Face Transformers, OpenAI API, RAG pipelines).

  
  


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  Exposure to text embeddings, prompt engineering, vector databases (e.g., FAISS, Pinecone, Chroma).

  
  


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  Proficient in using SQL and working with large datasets.

  
  


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  Familiarity with model deployment tools and practices (Docker, FastAPI, MLflow, Streamlit, or similar).

  
  


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  Experience with cloud platforms (AWS, Azure, or GCP) is a strong plus.

  
  

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  Experience with NLP, Computer Vision, or time-series modeling.

  
  


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  Exposure to MLOps workflows (CI/CD, monitoring, pipeline automation).

  
  


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  Familiarity with vector search, RAG architecture, and document intelligence workflows.

  
  


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  Previous experience in domains like BFSI, healthcare, retail, or enterprise automation is a plus.

  
  

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  Strong business acumen and stakeholder communication.

  
  


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  Ability to balance research mindset with production-readiness.

  
  


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  Passion for innovation, continuous learning, and cross-functional collaboration.

  
  


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  Team player with mentoring and leadership qualities.