Lead Quantitative Researcher - QIS

Lead Quantitative Researcher - QIS About us At RavenPack, we are at the forefront of developing the next generation of generative AI tools for the finance industry and beyond. With 20 years of experience as a leading big data analytics provider for financial services, we empower our clients—including some of the world's most successful hedge funds, banks, and asset managers—to enhance returns, reduce risk, and increase efficiency by integrating public information into their models and workflows. Building on this expertise, we are launching a new suite of GenAI and SaaS services, designed specifically for financial professionals. Join a Company that is Powering the Future of Finance with AI RavenPack has been recognized as the Best Alternative Data Provider by WatersTechnology and has been included in this year’s Top 100 Next Unicorns by Viva Technology. We have recently launched Bigdata.com , a next-generation platform aimed at transforming financial decision-making. About the role: As a Lead Quantitative Researcher under the Data Science - QIS team, you will be driving the development of new systematic trading strategies while engaging with clients to showcase the value of our data for trading and investment purposes across equities and macro, spanning multiple time horizons. You will play a vital role in the Quantitative Investment Strategies (QIS) Team, which consists of three quant researchers dedicated to feature engineering and developing systematic trading strategies. Your responsibilities will involve bottom-up research, complemented by some top-down opportunities. As part of this role, you will create white papers that enhance RavenPack’s reputation as a thought leader in the alternative data industry and present robust trading strategies to quantitative analysts at client firms. You will also work independently on practical use cases that demonstrate the value of RavenPack data. Key teams for collaboration will include Marketing and Sales. This role offers a hybrid work environment in our Marbella office. Key Responsabilities: • Identify, validate, and amplify predictive signals within our datasets while discerning and filtering out irrelevant information to improve decision-making processes. • Formulate systematic trading strategies spanning multiple asset classes, particularly focusing on equities, to enhance security-selection capabilities using Alternative Data over various holding periods. • Drive new feature engineering processes, effectively utilizing our analytics products and annotations, which require experience with enriched textual content. • Offer data-driven insights and actively engage in discussions about your research to present trading strategies to distinguished quantitative researchers and portfolio managers in the industry. • Communicate complex analytical concepts to management in a clear and concise manner, ensuring comprehension and actionable insights. What We're Looking For • A PhD or MSc in Quantitative or Computational Finance, or any related fields including Machine Learning, Econometrics, Applied Mathematics, etc. is essential. • A minimum of 5 years of relevant work experience as a quantitative researcher, including expertise in manipulating large and noisy alternative datasets for feature engineering, signal amplification, and portfolio backtesting. • Outstanding quantitative, analytical, and problem-solving skills with a proven ability to develop original research and conduct hypothesis testing. • Demonstrated proficiency in at least Python and SQL. • A strong enthusiasm for finance and technology, with familiarity in big data technologies and proficiency in machine learning as highly advantageous traits. What's in it for you? • International Culture: With its headquarters in Marbella, Spain, and a presence in Madrid, New York, and London, RavenPack takes pride in being a truly diverse global organization. • Competitive Salary: In RavenPack, we believe that your time and experience need to be fairly rewarded. • Continuous learning: We provide the support needed to grow within the team. • Innovation: Innovation is the key to our success, so we encourage you to speak up and tell us about your vision. • Relocation Assistance available for relocation into our Marbella office: Comprehensive relocation support is available to help you and your family move to the beautiful Costa del Sol. • Marbella Shuttle bus: From Malaga, Fuengirola, La Riviera, and Estepona is available for free from the company. We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. Department Data Science Locations Marbella Remote status Hybrid About RavenPack RavenPack is a global leader in financial data and intelligence, helping organizations turn vast amounts of unstructured information into decision-ready insight. Since 2003, RavenPack has focused on solving one of finance’s most persistent challenges: data is abundant, but usable data is scarce . By removing data friction across the entire lifecycle - from sourcing and preparation to deployment and action - RavenPack enables faster, clearer, and more confident decisions in high-stakes environments. Trusted by leading global financial institutions and used by approximately 70% of the world’s top-performing hedge funds, RavenPack combines deep financial domain expertise with proven data engineering and AI capabilities at scale. With more than 200 employees across the U.S. and Europe, the company has established itself as a gold standard for trusted, auditable, and production-ready financial intelligence. RavenPack offers two complementary products: RavenPack Edge , designed for quantitative and systematic investment workflows, and Bigdata.com , an AI-native platform for financial research and agentic systems built on trusted data. Bigdata.com enables professionals to deploy ready-made AI agents for instant insights or build their own via APIs—speeding up research, automating workflows, and driving better investment decisions. Founded in 2003