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.
Compensaciones / Incentivos
- International culture
- Competitive salary
- Continuous learning
- Innovation culture
- Relocation assistance to Marbella
- Marbella shuttle bus
Responsabilidades
- 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
Requisitos principales
- 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
- Machine Learning