Data Scientist · Hyderabad

Key Responsibilities

• Develop and Fine-tune LLMs: Train and adapt large language models for domain-specific applications such as policy summarization, claims document understanding, and automated customer responses.
  
• Generative AI Solutions: Build solutions leveraging text, image, and structured data to automate underwriting notes, claim justifications, and internal knowledge retrieval.
  
• Data Engineering & Model Pipeline: Work with data engineers to prepare, clean, and label unstructured insurance data (PDFs, scanned forms, emails, voice transcripts). Assist in collecting, cleaning, and preprocessing data for analysis and modelling.
  
• Participate in cross-functional discussions and strategic decision-making related to AI directions and product architectures.
  
• Prompt Engineering: Design, optimize, and evaluate prompts for LLM-based tools to ensure accuracy, compliance, and consistency in outputs.
  
• Integration with Business Systems: Deploy AI models into production environments integrated with CRM, claims management, and policy administration systems.
  
• Responsible AI and Governance: Ensure fairness, transparency, and compliance with data privacy and insurance regulations when using GenAI models.
  
• Cross-functional Collaboration: Work closely with product management, underwriters, claims managers, actuaries, and customer service teams to identify automation and augmentation opportunities.
  
• Innovation and Research: Stay current on emerging GenAI technologies (RAG, fine-tuning, multi modal models, vector databases) and pilot them within business contexts.
  

Required Qualifications

• Master’s or bachelor’s degree in computer science, Data Science, Statistics, AI, or related field.
  
• 2–5 years of experience in data science, NLP, or AI roles — with at least 1 year hands-on experience with GenAI / LLM-based solutions and production-grade AI/ML applications
  
• Strong programming experience in Python, using frameworks such as PyTorch, TensorFlow, LangChain, Hugging Face, or OpenAI APIs, FastAPI, Git, Linux, and Docker.
  
• Proficiency in SQL, data wrangling, and cloud environments (AWS, Azure, or GCP).
  
• Strong grounding in machine learning, transformer architectures, and vector database technologies
  
• Demonstrated experience with document understanding, summarization, Q&A, or conversational AI projects.
  

Preferred / Nice-to-Have

• Domain expertise in insurance, health tech, or financial services.
  
• Experience deploying AI applications in secure, regulated environments.
  
• Familiarity with RAG pipelines, LLMOps, or model evaluation frameworks.
  
• Understanding of OCR, knowledge graphs, and semantic search.
  
• Publications, hackathon wins, or open-source GenAI project contributions.
  

Key Metrics of Success

• Accuracy, efficiency, and ROI of deployed GenAI models.
  
• Reduction in manual effort for underwriting/claims/customer service teams.
  
• Adoption and satisfaction levels of AI tools among internal users.
  
• Compliance and governance adherence in GenAI outputs.