Senior Quantitative Researcher
Pubblicato il 07-09-2026 - RavenPack 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/N