Daniel Partida is a visionary data-driven entrepreneur and technology leader with a proven track record of building impactful products and scaling data organizations. With expertise spanning web3, AI/ML, and quantitative development, he has successfully founded venture-backed startups, secured significant funding, and led high-performing data teams to deliver actionable insights and drive strategic decisions. His unique blend of technical depth, entrepreneurial drive, and academic rigor positions him to innovate and lead at the forefront of data and product development.
Head of Data
Zurich, Zurich, Switzerland
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Summary
Led the data strategy and team for a web3 unicorn, driving data maturity and enabling data-driven decision-making across the organization.
Highlights
Designed and implemented a comprehensive data strategy, significantly boosting organizational data maturity and fostering a data-driven culture that enabled informed decision-making across product, marketing, and executive teams.
Built and led a high-performing data team encompassing product analytics, data engineering, and data science, delivering critical, actionable insights that empowered C-suite, product, and marketing functions within the Safe ecosystem.
Co-Founder & CTO
Zurich, Zurich, Switzerland
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Summary
As Co-Founder & CTO, Daniel Partida is actively designing and implementing a new paradigm for venture building, leveraging his expertise in data strategy and product development.
Highlights
Designing and implementing a new paradigm for venture building, focusing on innovative strategies for product development and market entry.
Sr. Research Associate
Zürich, Zürich, Switzerland
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Summary
Conducted advanced research at the University of Zürich, resulting in peer-reviewed publications demonstrating how offchain data predicts crypto market regime changes.
Highlights
Authored and published two peer-reviewed papers in the Journal of Digital Finance and the HICSS Conference with Prof. Parra (IMD Business School), demonstrating the predictive power of offchain data on crypto market regime changes.
Co-Founder & CTO
Berlin, Berlin, Germany
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Summary
Co-founded a web3 SaaS analytics startup, securing significant seed funding and leading the development of a high-scale data platform for crypto investors.
Highlights
Founded a web3 SaaS analytics startup, aggregating millions of off-chain data points from Twitter, Reddit, and Discord, and leveraging NLP models to provide crypto investors with actionable, data-driven investment insights.
Successfully raised a pre-seed round of $500K in commitments from venture capital Entrepreneur First, Plug & Play Ventures, and angel investors across Europe and Silicon Valley.
Directed a 7-person Engineering, Product, and Design (EPD) team in deploying a high-scale streaming pipeline capable of ingesting millions of off-chain data points daily.
Data Scientist
Berlin, Berlin, Germany
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Summary
Developed and deployed advanced machine learning and NLP solutions to enhance talent identification and match analytics for a Bundesliga club.
Highlights
Engineered an unsupervised ML pipeline, incorporating hierarchical clustering and recommender systems, to streamline player discovery and identify emerging talent for a top-tier Champions League club.
Created a real-time API crawler for a Champions League club, effectively aggregating data and detecting anomalies across multiple feeds to significantly enhance match analytics.
Implemented an NLP solution with Elasticsearch to analyze and prioritize thousands of scouting reports, enabling a Bundesliga club to efficiently pinpoint top prospects for its youth academy.
Quantitative Developer
Zug, Zug, Switzerland
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Summary
Developed predictive financial models for crypto returns and stock bubble detection, outperforming benchmarks and identifying market risks.
Highlights
Developed and deployed a predictive crypto returns model utilizing random forest and gradient boosting trees, consistently outperforming benchmark crypto ETFs.
Pioneered a stock bubble prediction model, integrating economic theory and behavioral finance to accurately identify and mitigate market risks.
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M.Sc.
Computational Science & Engineering
Grade: Top 10% of class
Courses
Dept. Applied Mathematics
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Research Exchange Year
Computational Mechanics
Courses
Dept. Computational Mechanics
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B.Sc.
Computational Engineering Science
Courses
Major: Computer Science
Minor: Mathematics
Awarded By
Tokyo Institute of Technology
Awarded for academic excellence during the Research Exchange Year.
Awarded By
RWTH Aachen University
Awarded for academic merit during Bachelor of Science studies.
Published by
Journal in Digital Finance and HICSS Conference
Summary
Co-authored two peer-reviewed papers with Prof. Parra (IMD Business School) demonstrating how offchain data predicts regime changes in crypto markets.
Leadership, Strategic, Teamwork, Efficient, Innovative.
Data-driven, Python, ML, NLP, LLMs, SQL, NoSQL, Elasticsearch, REST API, Cloud, Docker, MLOps, DevOps.