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 cultureContinuous learningRelocation assistance to MarbellaIdentify and filter predictive signals in datasets to support better decision-makingDesign systematic trading strategies across asset classes,
with a focus on equitiesAdvance feature engineering using analytics products and enriched textual contentPresent data-driven research and trading strategies to peers and portfolio managersCommunicate complex analytics concepts clearly to management with actionable insightsPhD in Quantitative or Computational Finance or related fields including Machine Learning, Econometrics, Applied Mathematics (essential)Minimum 5 years of experience as a quantitative researcherProficiency in Python and SQLExperience handling large, noisy alternative datasets for feature engineering and backtestingStrong analytical and problem-solving skills with ability to conduct hypothesis testingCommunication with technical and non-technical stakeholdersCollaborative mindset for cross-team workEnthusiasm for finance and technologyPythonSQL
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