Senior Quantitative Researcher
Pubblicato il 26-08-2026 - RavenPack in Italia
RavenPack is powering the future of finance with AI. With over 20 years of experience delivering big data analytics to the world’s top hedge funds, banks, and asset managers, we are now building the next generation of generative AI solutions for financial professionals.
Help clients make faster, smarter decisions by integrating public information into their workflows using cutting-edge NLP, ML, and GenAI technologies. RavenPack has been recognized as the Best Alternative Data Provider (WatersTechnology) and one of VivaTech’s Top 100 Next Unicorns.
The Opportunity We’re seeking a Senior Quantitative Researcher to drive quantitative investment use cases across RavenPack’s product suite, from creating alpha-generating datasets to developing intelligent agents and workflow solutions that transform how finance professionals operate.
You’ll combine deep technical expertise with a strong quantitative finance background to lead POC development, guide applied research, and work directly with enterprise clients to deliver real-world AI solutions in finance.
European legal working status is required.
Relocation Assistance : Comprehensive relocation support is available to help you and your family move to the beautiful Costa del Sol.
Execute Strategy: apply LLMs/LRMs, fine-tuning, and multi-agent systems to tackle complex investment challenges.
Advance Research: Stay ahead of AI/ML trends and lead internal R&D; aligned with strategic business goals.
Collaborate Across Teams: Work closely with engineers, product managers, and executives to deliver scalable solutions.
MSc or PhD in Computer Science, Machine Learning, NLP, Quantitative Finance, or related field.
~3+ years experience in quantitative investment strategies from buyside or sell-side, with proven success in ML/NLP-driven initiatives.
~ Hands-on experience with LLMs, LRMs, and coding assistant technologies.
~ Background in alpha generation, time-series modeling, workflow orchestration, and multi-agent LLM architectur