In this role you drive the development of systematic trading strategies using RavenPack's alternative data, collaborating with cross-functional teams to showcase data value to traders and investors. You will lead feature engineering and model development within the QIS team, delivering research-driven insights and practical use cases for clients. You’ll publish white papers to position RavenPack as a thought leader and present strategy results to quantitative analysts. This hybrid role combines independent research with client-facing impact in a innovation-forward, finance-focused environment. International culture Continuous learning Relocation assistance to Marbella Identify and filter predictive signals in datasets to support better decision-making Design systematic trading strategies across asset classes,



with a focus on equities Advance feature engineering using analytics products and enriched textual content Present data-driven research and trading strategies to peers and portfolio managers Communicate complex analytics concepts clearly to management with actionable insights PhD in Quantitative or Computational Finance or related fields including Machine Learning, Econometrics, Applied Mathematics (essential) Minimum 5 years of experience as a quantitative researcher Proficiency in Python and SQL Experience handling large, noisy alternative datasets for feature engineering and backtesting Strong analytical and problem-solving skills with ability to conduct hypothesis testing Communication with technical and non-technical stakeholders Collaborative mindset for cross-team work Enthusiasm for finance and technology Python SQL

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📌 Lead Quantitative Researcher - Qis - Ravenpack (Madrid)
🏢 Ravenpack
📍 Madrid