Deb Bose
Research • Notebooks • Papers • Projects
I work at the intersection of investment research, data engineering, and machine learning, with governance built into the system rather than bolted on after. That means the analytics that inform an investment decision, the pipelines and models underneath them, and the controls that let a reviewer trust the whole thing all come from the same person, designed together.
Eighteen years shipping data platforms and quantitative systems in financial services, most recently leading data modernisation at Macquarie Group. CFA Level 1 and GARP-certified in Risk and AI (RAI), so neither the finance nor the governance is a veneer. If you have an investment question that deserves a real system behind it, a data platform that needs to be trustworthy by design, or models in production that a regulator wants evidence for, let’s talk.
My working belief is that governance is a design property, not a compliance afterthought. Lineage, controls, and evidence are cheapest and most credible when they are built into the pipeline from the first commit, not reconstructed under audit.
The rest of this site is a working archive, not a feed. It exists because I think in public.
Everything here is oriented toward truth-seeking through analysis: quantitative reasoning where possible, first-principles thinking where necessary, and skepticism toward narratives that don’t survive contact with data.
Work with me
I take on a small number of engagements where the problem is concrete and the stakes are real:
- Investment research and quantitative analysis. Portfolio analytics, economic time-series, and financial modelling, grounded in CFA Level 1 fundamentals and turned into reproducible systems rather than one-off notebooks.
- Data engineering and platform modernisation. DBT, Iceberg, Redshift, and observable pipelines, built so the lineage, contracts, and controls are first-class from the first commit.
- End-to-end ML and AI delivery. Scoping, data, modelling, and deployment, owned through to a system that runs, is maintainable, and can be reasoned about.
- Data and AI governance by design. Lineage, model validation, drift and calibration monitoring, and audit trails designed into the pipeline, so evidence for CPG 235 / BCBS 239 reviewers is a by-product of how the system is built, not a project of its own.
If that maps to a problem you have, email bose.debasish@gmail.com or read more about my background.
What you’ll find here
Articles
Essays, market notes, and technical deep dives, written to clarify, not to perform.
Notebooks
Runnable research with code, charts, and assumptions made explicit.
Exploratory by design, not polished marketing artefacts.
Papers
Drafts, PDFs, and reference material, including work that is incomplete, evolving, or deliberately unresolved.
Projects
Pointers to my GitHub work: tools, models, and systems built to answer specific questions.