Quant Engineer: Data Products

• 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.

• Quantitative research background, academic or industry.
• Index construction and classification taxonomies.
• Working with model-derived inputs, understanding that NLP-generated exposures carry measurement error and revise over time.
• Task orchestration (Dagster or Airflow) and S3-based data flows.
• AWS fluency and CI/CD discipline.

• Competitive salary plus share options
• 25 working days holiday, plus Spanish public holidays
• Hybrid working from Barcelona

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.

• 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.