Senior Data Scientist - Conversational AI

Responsibilities:

• Building and owning a conversational AI research assistant for retail investors in Indian capital markets, think Claude but specialised for different instruments and macro analysis for non-expert users.
• Designing multi-agent agentic pipelines with tool-calling, memory management, and multi-turn conversational flows that handle real, messy, incomplete queries from retail users (not just clean, structured prompts from analysts).
• RAG pipeline architecture: source curation, chunking strategy, embedding quality, retrieval tuning, reranking, and citation-grounded responses that retail users can trust.
• Integrating real-time financial data sources (NSE/BSE feeds, Screener, Tickertape, news APIs, company filings) as live tool-callable data layers, not just static retrieval.
• Building the guardrails and evaluation layer: domain scoping, hallucination mitigation, confidence scoring, and monitoring to ensure the system stays accurate and within bounds over time.
• Fine-tuning or adapting LLMs where retrieval alone isn't sufficient.
• Building ML models for user behaviour, personalisation, and financial insights that feed into the conversational layer.

Requirements:

• 4-8 years of hands-on experience in Data Science, Machine Learning, or Applied AI.
• Capital Markets/WealthTech domain experience is highly preferred, or candidates who have built AI research assistants for financial products or have strong personal knowledge of investing/trading.
• Understanding of large language models (LLMs) like LLAMA, Anthropic Claude 3 or Sonnet.
• Familiarity with cloud platforms for data science like AWS Bedrock and GCP Vertex AI.
• Strong proficiency in Python and data science libraries (scikit-learn, TensorFlow, PyTorch).
• Solid understanding of statistical methods, machine learning algorithms, and wealth tech applications.
• Experience in data wrangling, visualisation, and analysis.

Good to Have:

• Experience in capital market use cases.
• Familiarity with recommender systems and personalisation techniques.
• Experience building and deploying production models.
• Data science project portfolio or contributions to open-source libraries.
• Experience with embedding models and retrieval quality improvement.
• Worked at an AI-first startup in any domain.