Lead Data Scientist - Quantitative Investment Strategies
Lead Data Scientist - Quantitative Investment Strategies
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Job Description
About us
RavenPack is powering the future of finance with AI. With over 20 years of experience delivering big data analytics to the worlds top hedge funds banks and asset managers we are now building the next generation of generative AI solutions for financial professionals.
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 VivaTechs Top 100 Next Unicorns.
The OpportunityWere seeking a senior leader to drive quantitative investment use cases across RavenPacks product suite from creating alpha-generating datasets to developing intelligent agents and workflow solutions that transform how finance professionals operate.
This role reports directly to Peter Hafez Chief Data Scientist at RavenPack. Youll 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.
What Youll DoLead Innovation:
- 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.
- Shape Strategy: Drive adoption of 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 Across Teams: Work closely with engineers product managers and executives to deliver scalable solutions.
- Champion Communication: Represent RavenPack at industry conferences developer communities and client events translating complex ideas into impactful narratives.
What Were Looking For
- MSc or PhD in Computer Science Machine Learning NLP Quantitative Finance or related field.
- 7 years experience in quantitative investment strategies from buyside or sell-side with proven success in ML/NLP-driven initiatives.
- Deep hands-on expertise with LLMs LRMs and coding assistant technologies.
- Strong background in alpha generation time-series modeling workflow orchestration and multi-agent LLM architectures.
- Proficiency in Python (plus SQL or other databases).Excellent client-facing and project leadership mindset with strong business acumen and a passion for pushing the boundaries of applied AI in finance.
Why Join Us
At RavenPack youll 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 to leading industry players and cutting-edge technology.
Competitive compensation and relocation support.
Ready to lead the next wave of AI in finance Apply now and help us build the future.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.
Whats in it for You
- International Culture: With its headquarters in Marbella Spain and presence in Madrid New York and London RavenPack takes pride in being a truly diverse global organization.
- Best in Class: You will work with top engineers with experience using ReactJS Python Java and Lisp on cutting-edge innovative technology.
- Competitive Salary: In RavenPack we believe that your time and experience needs 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.
- Diversity is in our DNA! You will work in an international environment (over 29 nationalities and 24 languages spoken!)
Employment Type
Full Time
