Senior Data Scientist- Gen Ai

Company Vision

StockGro is a mobile-first cross-platform (Android & iOS, Mobile + Web App) Fintech product that's empowering 25 million+ users to master the art of trading and investment in a risk-free and gamified manner. At StockGro - India's First and Largest Social Investment Platform, users indulge in Social Investing and learn various trading strategies by interacting with leading fund managers, F&O traders, and algo traders.

About StockGro

Founded in January 2020 by former venture capitalist Ajay Lakhotia, we're well-funded and just closed a massive Pre-Series A fundraise. We are backed by some of the respected investors - Roots Ventures, Velo Partners, Creed Capital, and the likes of Kunal Shah, Vivekananda Hallekere, Rahul Garg as Angels.

We have some brilliant minds with us, working on a mission to make 400 million Indian millennials investment-ready, with Senior Executives from Sequoia, Swiggy, Glance, Airtel, Uber, and institutions like ISB, NITs, and IIMs.

What you'll work on:

Designing and building GenAI / LLM-based systems for wealth advisory use cases (personalised

insights, portfolio intelligence, conversational advisory)

Prompt engineering, RAG pipelines, embeddings, vector databases

Fine-tuning / adapting LLMs where required

Building ML models for user behaviour, recommendations, and financial insights

End-to-end ownership: data exploration modelling deployment monitoring

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.