Earlybird’s Engineering Team Welcomes 3 New Joiners
As Venture Capital becomes more data-driven, automating processes and parsing a high volume of information — sorting signal vs. noise — and opening investment opportunities to more founders, becomes increasingly valuable. VCs evaluate startup teams composed of people with a range of intrinsic and extrinsic motivations and diverse experiences. So the future of investment arguably requires an augmented approach: one that mixes algorithms with human nuance.
With that, we’re excited to introduce three new joiners in Earlybird’s growing Engineering team: Daniel Dakota, Luca Tabone and Ludvig Wärnberg Gerdin. The key focus of this team is the development of EagleEye. Initiated and led by our Partner Andre Retterath, EagleEye is a proprietary AI-powered platform long in the making. Through gathering and analyzing data, it extends our human capabilities, ultimately making our industry more effective, efficient and inclusive.
Get to know our new Engineering teammates’ individual backgrounds and contributions below in this Q&A.
Daniel Dakota
Daniel: You have a background as an NLP engineer, data scientist, and product manager. How do you combine that in leading the Machine Learning Team at Earlybird? As Deep Learning models are adopted by more organizations to extract knowledge, and NLP grows in market value and importance, can you explain how ‘Natural Language Processing (NLP) engineering’ is applied in the VC context ?
DD: Executing solutions from various perspectives has allowed me extensive hands-on experience in different levels of technical understanding, business strategy and communication skills. These are all pivotal for a smaller, developing team. My primary background in machine learning is NLP — a subfield of AI that works on teaching computers how to analyze and understand natural human language. We can apply these techniques to diverse textual sources (e.g. social media, pitches, or internal notes) to provide data insights for investment analysts, helping them with lead identification and prioritization.
Luca Tabone
Luca: You speak many computer languages and lead engineering here. Since you seem keen on a wide range of topics: AI, Data Mining, Software Architectures, Modeling, Computational Genomics, how do you blend those interests in daily work? Can you give us more insights into Earybird’s EagleEye?
I love creating software. Humans have a great capacity and natural longing to be engaged in creative processes. If we can provide value to others while doing what we love, especially in a team of open and bright minds, work becomes something that fuels your personal flourishing.
Before joining Earlybird, I had no idea about the company culture and had doubts if VC was the place for me; I didn’t know anyone in Finance in general. As a teenager though, I had thoughts of becoming an investment banker. So sector interest was there. After interviews with Andre and the wider team, I got a good feeling about the firm and EagleEye. This opportunity was too exciting to pass up.
Here’s a sneak peak into what we’re creating with EagleEye: Earlybird invests into teams, or companies, who are eager to change the world. VC is inherently a risky business, so our investment team does significant research before adding a portfolio company. Finding these companies in the first place is a time consuming challenge! EagleEye changes that by analyzing data of millions of companies, discovering startup origin and traction. With the help of intelligent scores and filters, startups are brought onto the radar of our investment team who engage in further research and make final investment decisions. Moreover, it supports our portfolio value creation initiatives.
In a nutshell: EagleEye increases the probability of finding black swans (promising startups) and helps our investment team save time and make more data-driven decisions.
Ludvig Wärnberg Gerdin
Ludvig: You’ve actually been with Earlybird since January 2022. During the spring, you were writing your master’s thesis on the topic of startup success prediction. Can you tell us how your thesis plays into your work, and what your next challenge tackles here?
LWG: During my thesis I worked on improving the Earlybird pre-screening model that reveals the probability that a startup will succeed within a certain time frame. The refined model places emphasis on any startup team’s background, such as academic and professional background, allowing us to employ the model to prioritize which startups to look deeper into, even before the initial call with the founders. In parallel, I iterated on the machine learning system architecture, implementing systems that allow continuous re-training and scoring.
Going forward, my work will mainly revolve around integrating new data sources and further improving the data and scoring architecture to allow for fast iteration in downstream business analytics and modeling!
Thanks for sharing these insights on your work with us. We look forward to hearing from you more in 2023.
To read insights from our team, check out Earlybird’s View Publication.
