Data Scientist

What youll work on:

• Building and owning a conversational AI research assistant for retail investors in Indian capital markets, think Claude but specialized 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, personalization, and financial insights that feed into the conversational layer

Expectations :

• 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, visualization, and analysis.
• Collaborative mindset and ability to thrive in a fast-paced startup environment.

Bonus points:

• Experience in capital market usecases
• Familiarity with recommender systems and personalization 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