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.
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.
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
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
PyTorch useful)
Datasets in pandas and Parquet/Arrow, plus an analytical engine such as DuckDB
Task orchestration (Dagster or Airflow) and S3-based data flows
AWS fluency and CI/CD discipline
25 working days holiday, plus Spanish public holidays
~ Hybrid working from Barcelona

📌 Quant Engineer: Data Products (Cataluña)
🏢 Theia Insights
📍 Cataluña