Monetize Transaction Intelligence: Design and develop inventive products that leverage AI/ML/DL models to transform data into actionable investment signals and alpha-generating opportunities.
Architect Financial AI: Build custom GenAI, NLP, and LLM models for high-velocity stream processing, focusing on extracting market sentiment and spending trends from structured transaction data and unstructured metadata.
Next-Gen Frameworks: Implement LangChain and LlamaIndex to develop RAG and Agentic AI frameworks that enables interaction with complex payment datasets.
Quantitative Collaboration: Work in a high-performance team environment, collaborating with Product Managers, Payment System Experts and Engineering to deploy and monitor production-grade AI & ML models.
Strategic Synthesis: Distill complex quantitative insights into high-level investment theses for executive leadership and sophisticated external stakeholders.
Data Stewardship & Compliance: Partner with the Data Usage Committee, Legal, and Compliance teams to ensure data privacy and adherence to strict data usage rights.

Bachelor’s degree in a highly quantitative field such as Computer Science, Mathematics, Artificial Intelligence, Financial Engineering, or Statistics.
5+ years of experience leveraging massive structured (transactional) and unstructured datasets to develop tactical investment insights using ML, RAG, and NLP.
5+ years of experience formulating research problems, designing back tests, and implementing production-ready solutions in a financial or high-growth tech environment.
Proficiency in tokenization and embeddings, with hands-on experience tuning and deploying Large Language Model architectures (e.g., LLaMA, BERT, or Transformers) for financial domain tasks.
Experience with Python, PyTorch, TensorFlow, and Agentic AI frameworks.
Deep familiarity with Time Series Econometrics, Quantitative Investment Strategies, and Alternative Data (specifically merchant and banking data).
Mastery of statistical techniques including regression, classification, clustering, and non-stationary time series analysis.
Working knowledge of Databricks, Snowflake, or high-performance database systems, and experience with Azure ML, AWS SageMaker or IBM Watson.

Senior Data Scientist – Capital Markets

Key skills