GenAI Data Scientist/ Financial Services/ Capital Markets / NYC (Hybrid)/W2

We are looking for a GenAI-focused Data Scientist with strong financial services / capital markets experience to work on next-generation data and AI products. The ideal candidate will have an advanced degree in Financial Engineering or Data Science and hands-on experience building machine learning and Generative AI solutions in a production environment.

This role requires someone who already understands the financial business context-we are not looking to train from scratch on capital markets.

• Collaborate closely with product development and technology teams to advance S3 data products

• Design, improve, and innovate statistical, machine learning, and predictive models across asset classes such as Equities and Fixed Income

• Develop and deploy AI/ML predictive analytics models

• Work with Generative AI models, including LLMs, embeddings, and prompt-based workflows

• Evaluate and select appropriate GenAI models (open-source or commercial) for business use cases

• Perform daily data quality checks and ensure production stability

• Support model performance monitoring and continuous improvement

• Advanced degree in Financial Engineering or Data Science (MANDATORY)

• 4+ years of experience in data analysis, data science, or a similar role

• Strong experience applying machine learning techniques in real-world scenarios

• Hands-on experience with LLMs / GenAI technologies

• Experience using Azure AI / OpenAI APIs

• Familiarity with LangChain or similar orchestration frameworks

• Strong programming skills in Python and SQL

• Experience with Azure ML, Excel

• Financial Services / Capital Markets experience is REQUIRED

• Experience with fine-tuning models, RAG (Retrieval-Augmented Generation), and model optimization

• Understanding of model governance, Responsible AI, and risk controls in financial environments

• Experience in Prime Brokerage, Securities Lending, or Counterparty Risk

• Work on cutting-edge GenAI and ML initiatives

• Exposure to capital markets data and complex financial use cases

• Competitive compensation and long-term growth opportunity