Senior Data Scientist- Conversational AI

StockGro; Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

StockGro in Bengaluru is seeking a skilled data scientist to develop a conversational AI research assistant tailored for retail investors in the Indian capital markets. The role involves designing complex multi-agent pipelines, integrating real-time financial data, and building ML models for personalized insights.

Candidates should have strong expertise in Python, experience with cloud platforms like AWS or GCP, and a solid understanding of machine learning algorithms. A collaborative mindset and ability to thrive in a fast-paced startup environment are essential.

Qualifications

• Experience in building and deploying production models.
• Knowledge of recommender systems and personalization techniques.
• Project portfolio demonstrating data science skills.

Responsibilities

• Build and own a conversational AI research assistant for retail investors.
• Design multi-agent pipelines with memory management.
• Integrate real-time financial data sources.
• Build ML models for user behaviour and insights.

Skills

Conversational AI development

Cloud platform experience (AWS, GCP)

Proficiency in Python

Machine learning algorithms

Data wrangling and analysis

Statistical methods

Familiarity with LLMs

Tools

TensorFlow

PyTorch

scikit-learn

Job description

What you’ll 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
• 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.
• 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