Theia Insights builds foundational financial intelligence products, including industry classification, knowledge graphs and factor risk models, for institutional investors. We serve some of the largest asset managers, hedge funds, index providers and sell-side banks. As a quant engineer on the Data Products team you'll build and run the models behind our Thematic Factor Risk Models (TFM): decomposing stock returns into thematic and traditional risk factors, back-testing methodologies and turning research into daily production output alongside our economics team. The Data Products team owns the data that underpins everything we sell. It's a small, senior group that values correctness and reproducibility over volume, and it sits close to the product leads who shape the methodology.
What You'll Do
Develop statistical models of stock price movements and estimate the performance of thematic trends
Construct and back-test factor risk models, decomposing stock returns into thematic and traditional risk factors
Design and validate signal-generation and portfolio-attribution methodologies in collaboration with the economics team
Make research reproducible, so that any published output can be re-run exactly, including after backfills and restatements
Work with the pipelines team to take modelling decisions into daily production
Requirements
Essential
Strong production Python
Factor risk models and portfolio attribution in depth: cross-sectional regression, covariance estimation and shrinkage, and back-tests you'd defend line by line
Point-in-time discipline, look-ahead and survivorship bias, and reconstructing what was knowable on a given date
Statistical modelling and optimisation (statsmodels, cvxpy; PyTorch useful)
Datasets in pandas and Parquet/Arrow, plus an analytical engine such as DuckDB
Nice to have
Quantitative research background, academic or industry
Index construction and classification taxonomies
Working with model-derived inputs, understa
📌 Quant Engineer: Data Products (Barcelona)
🏢 Theia Insights
📍 Barcelona
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