Reproducible by conviction
Hi there, I'm Christoph Scheuch!
I'm a financial economist who has spent the last decade moving between academia and industry. One thing has stayed constant: I care that research and business decisions can be traced back to the raw data.
Today I wear three hats. I research and teach at HU Berlin. I serve as Data Editor at the Review of Financial Studies, where I verify that published research is reproducible from its replication package. And through Tidy Intelligence I help banks, asset managers, and research teams make their own quantitative work just as auditable. Before this, I led AI, product, and data science teams at a fintech, so I know what it takes to make reproducibility work inside a real organization.
I co-created Tidy Finance and EconDataverse — open resources for reproducible research in economics and finance — and I write open-source packages like tidyfinance, fmpapi, and wbids in R, plus wbwdi and fmpapi in Python, so analysts can spend less time fighting data and more time generating insight.
Research Review
Whether you're preparing a paper for submission, an internal model for sign-off, or a published analysis for an audit, I review your data, code, and methodology end-to-end. I confirm that results can be reproduced from the raw inputs, identify gaps in documentation, and flag where assumptions, manual steps, or undocumented choices could come back to haunt you.
My perspective combines two roles. As Data Editor at the Review of Financial Studies, I verify replication packages for one of the leading journals in financial economics. As a former Head of BI & Data Science at a fintech, I know what auditors, regulators, and internal stakeholders actually ask for. The deliverable is a concrete report: what works, what doesn't, and what to fix first.
Hands-On Training
I deliver tailored training on reproducible workflows for analysts, researchers, and graduate students — the practices that make sure today's analysis still runs tomorrow, on a colleague's machine, with the same numbers. Sessions blend theoretical foundations with hands-on exercises on real data, so participants leave with skills they can apply the next day.
Typical topics include project structure and version control, Quarto-based literate analysis, data engineering with R or Python, and reproducible publication-quality outputs. I currently teach reproducible research at HU Berlin, the Barcelona School of Economics, and the Vienna Graduate School of Finance, and I adapt content and pace for academic groups and industry teams alike.
AI Audit & Compliance
The EU AI Act and adjacent model risk frameworks now require what good research has always required: that AI and ML systems be documented, testable, and reproducible. I help banks, asset managers, and research teams assess their AI workflows against these standards, identify gaps in data lineage, model documentation, and validation, and prioritize the fixes that matter for both compliance and trust.
I bring direct experience from both sides: I led AI product development at a fintech, and I now study and write about transparent, auditable AI as a researcher. The result is an audit that is both technically rigorous and practical — it tells you what regulators will look for, and what your own analysts need to sleep at night.
Selected Publications
Books and research articles on reproducible methods and empirical finance.
Tidy Finance with R — with Stefan Voigt & Patrick Weiss — Chapman & Hall/CRC
The original textbook introducing a transparent, open-source approach to reproducible methods for empirical problems in financial economics.
Tidy Finance with Python — with Stefan Voigt, Patrick Weiss & Christoph Frey — Chapman & Hall/CRC
A Python translation of the updated edition of our textbook on reproducible methods for empirical problems in financial economics.
tidyfinance: Transparent Factor Construction for Empirical Asset Pricing — with Stefan Voigt & Patrick Weiss — Working Paper
A paper that provides research infrastructure for the empirical asset pricing data workflow.
Teaching
Current courses and workshops on reproducible research and empirical finance.
Foundations for Reproducible Research — Barcelona School of Economics
An annual summer school on reproducible empirical finance research, with hands-on coding in Python.
Empirical Research in Finance — Humboldt University of Berlin
A seminar on applying core financial theory to real-world data using generative AI tools, designed to prepare students for their bachelor thesis.
Reproducible Research Workflows — Vienna Graduate School of Finance
An annual workshop on reproducibility techniques and efficient collaboration for empirical research.
Get in touch
Message goes here
