Senior Quantitative Researcher
Pubblicato il 10-09-2026 - Altro in Milano
Our mission: 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.
What You'll Do:
Design and build ML/NLP-powered solutions that generate measurable value in alpha generation and financial workflows.
Engage Clients: Partner with top-tier financial institutions during POCs and pilots, rapidly iterating solutions based on real-world feedback.
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.
Communication: Represent RavenPack at industry conferences, developer communities, and client events, translating complex ideas into impactful narratives.
What We're Looking For 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 initiat