Quantitative Systems for Credit .
I help credit funds and specialty lenders build the models, data infrastructure, and automated workflows that let small teams deploy more capital — without adding analysts.
and structured finance teams
Senior analysts spending a significant portion of their time on data wrangling, reconciliation, and report production instead of investment work.
Critical portfolio data scattered across Excel, PDFs, fund admin systems, and inboxes, with no single source of truth.
Manual processes that worked at €200M AUM breaking down at €1B+ — and replacing them feels too risky to start.
Data Infrastructure
Designing and building the systems that turn heterogeneous financial data into reliable, structured outputs — across formats, standards, and jurisdictions.
Quantitative Modeling
Valuation, risk, stress testing, and cash flow modeling for credit and structured finance. From single-asset analysis to portfolio-level frameworks.
Process Automation
Replacing manual workflows with scalable, automated pipelines. Turning recurring operational bottlenecks into systems that run without intervention.
AI / ML Implementation
Applying modern techniques to financial data problems where traditional approaches break down — document processing, classification, anomaly detection, NLP.
I built these systems inside Morgan Stanley for 5 years before going independent — I know what production-grade looks like in regulated finance.
I cover both the quantitative side (valuation, risk, stress testing) and the engineering side (data infrastructure, automation, ML, AI).
I work directly with the team, not through a layer of junior staff. You get the senior person on the actual work.