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.