Senior Quantitative Researcher - QIS

RavenPack; Brussel Hoofdstad; Sur place; EUR 90 000 - 140 000; Plein temps

Avantages offerts par ce poste

Relocation assistance

Competitive compensation

Résumé du poste

RavenPack is 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.

Qualifications

• 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 architectures.
• Proficiency in Python (plus SQL or other databases). Entrepreneurial mindset with strong business acumen and a passion for pushing the boundaries of applied AI in finance.

Responsabilités

• 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.

Connaissances

Python

ML/NLP

LLMs/LRMs

Time-series modeling

Multi-agent systems

Formation

MSc or PhD in CS/ML/NLP/Quant Finance

Outils

SQL

Description du poste

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 initiatives.


• Hands-on experience with LLMs, LRMs, and coding assistant technologies.


• Background in alpha generation, time-series modeling, workflow orchestration, and multi-agent LLM architectures.


• Proficiency in Python (plus SQL or other databases). Entrepreneurial mindset with strong business acumen and a passion for pushing the boundaries of applied AI in finance.

Why Join Us?

At RavenPack, you’ll work at the intersection of AI and quantitative finance, shaping the future of investment decision‑making in global markets.

We offer: A collaborative, international culture based in Marbella, Spain. Access to leading industry players and cutting-edge technology.

Competitive compensation and relocation support.

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.

What’s in it for you?

• Growth Opportunity: Join a rapidly growing company with 180+ team members with offices in New York, Madrid, and Marbella, building AI that truly matters for business and finance.


• International Culture: Be part of a diverse, global organization with a truly international culture.


• Team of Superstars: Work alongside a talented team of professionals, including Silicon Valley executives and Wall Street veterans.


• Competitive Compensation: We offer a highly competitive salary package and performance-based bonuses.


• Relocation Assistance: Comprehensive relocation support is available to help you and your family move to the beautiful Costa del Sol.


• Work-Life Balance: Enjoy paid vacation, flexible time‑off, and a flexible work policy.


• Continuous learning: We provide the support needed to grow within the team.