In this role you drive the development of systematic trading strategies using Raven Pack'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 Raven Pack 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 Ph D 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 #J-18808-Ljbffr

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