ROLE
– Build unsupervised and semi-supervised ML models to scan millions of transactional records for outliers.
– Detect complex patterns in transactional records.
– Reduce false positives to ensure the Reporting Team trusts the model alerts.
– Design RAG pipelines to “chat” with unstructured data and extract key regulatory attributes.
– Build “Agentic” workflows where GenAI proactively suggests mapping logic or identifies the root cause of a break.
– Create Validation Interfaces: Build simple UIs where business users can see the Model’s Prediction side-by-side with the Source Document.
– Act as the “AI Evangelist” to the Operations/Finance teams, demonstrating how AI assists them rather than replacing them.

REQUIREMENTS
– Core ML: 6+ years in Data Science/Engineering.
– Deep experience with Scikit-learn, TensorFlow, or PyTorch.
– The “Validation” Stack: Experience building tools like Streamlit or Gradio for rapid prototyping of human-review interfaces.
– Communication: Ability to explain technical concepts to non-technical stakeholders.
– Pragmatism: Ability to know when not to use AI.