Staff Data Scientist

About the role

Machine learning and agentic AI work across invoices, claims, payments and audit data, taking problems from exploration to production. The team mixes data science, data engineering and software engineering to catch financial anomalies and automate decisions.

At a glance

What you will do

• Develop supervised and unsupervised models for anomaly detection, classification, clustering, ranking and prediction.
• Create root cause analysis, signal correlation and explainability methods for financial anomalies and audit findings.
• Build LLM applications, RAG experiences, tool calling and agentic workflows that use approved data and tools.
• Design evaluation for ML and agentic systems covering performance, groundedness, tool use accuracy and safety.
• Work with data and software engineers to productionize, monitor, explain and retrain models.

What they look for

• Hands-on data science and machine learning experience, including statistical modeling and applied AI.
• Experience deploying supervised and unsupervised ML with feature engineering, experimentation, evaluation and explainability.
• Experience with agent orchestration frameworks such as LangGraph or Google ADK, plus tool integration through MCP or similar.
• Advanced Python and SQL, with modern ML frameworks and large transactional or financial datasets.

Nice to have

• Familiarity with MLOps practices.