Companies are always looking for the best tech talent on the market. Finding this talent is difficult, but thanks to David Mozes and his partners at Tech Rise this process is made more efficient. Together with a company they look for young talent on platforms like GitHub and StackOverflow to fulfil the needs of the company.
There are many definitions of artificial intelligence, but what is AI, according to you?
I think it’s old wine in new barrels. It is a way to use software and data, to make processes more intelligent and more efficient. Twenty years ago, we were simply talking about statistics. Since then, the computer processing power has increased enormously, making it easier to put the things you can get out of the data into beautiful formulas, and call those formulas algorithms. Ultimately, AI processes are made easier so that the work of humans can also be made easier.
Who are you, and what do you do?
I am David Mozes, a partner at Tech Rise. I’ve spent my whole life on the cutting edge of Tech and the stocks associated with it. At Kempen & Co, I started in equity sales; I advised on tech stocks such as ASML, Tomtom, etc. Then I started a company, LED Lease & Finance, which was in sustainability, but with intelligent financing. We were the first to convert expensive LED installations into light-as-a-service. People paid a fixed fee per year for a certain amount of light.
After that, I started working at Seedrs, one of the largest equity crowd platforms, enabling big groups to buy back a small number of shares. That’s how I returned to my roots which lay in the stocks. Later I joined Volta Ventures, which was a software as service investor. There I found out that money is important, but converting financial capital to human capital is even more critical. With this in mind, I started Tech Rise together with two partners.
Large companies such as Mollie and Adyen want the most outstanding tech talent. We support those companies, but quite often we are of even greater added value to tech scale-ups. If you look at smaller companies that want to proliferate and have money, we help those in their human capital domain. I look for investments in those kinds of excellent companies and investigate whether they are interesting enough to invest in and work with. We can be a double-barreled rifle where we help on the one hand with the financial capital and on the other hand with the rapid growth of the team, call it human capital.
What sets Tech Rise apart from other companies?
The most important thing is that we have a high scoring percentage. For all jobs we work on, we have a 91% success rate within 10 weeks. Our success is mostly based on our gravy investment in sourcing, and so on building talent pools within the tech domain. We have large communities of talent that we look for and we go way beyond LinkedIn. A ‘lazy’ recruiter will source via LinkedIn, but you must dig into other pools as well. For example, we also look at channels like Github, Reddit, and StackOverflow for talent, and encounter talent through events.
Furthermore, it is unique that, as a recruitment organization, we can be of added value to the financial domain. Companies come to us, looking for, for example, 2 million euros, and ask: ‘how am I going to do that?’. That’s not an average question you ask a recruiter, but we can help them with this question.
Can you tell us a bit about finding a company, analyzing their problem, and then finding new tech talent for them?
We work through about a thousand profiles per search. We make that completely transparent with an ATS (application tracking software) where the clients can monitor this process. The client can log in to an online board and also see the progress (potential) candidates make and give live feedback on profiles. These thousand profiles ultimately belong to a longlist. Together with the client, we work this down to a shortlist. Then there is a whole process of invitations and interviews to find qualified candidates. This is then narrowed down to six of seven candidates, from which a number of offers flow.
To find talent, we have embraced a model that reads the job description and extracts keywords and makes a correlation between those keywords and thousands of profiles. Based on this, about a thousand profiles are advised to analyze. Ultimately, the AI is not yet ready to reduce all these profiles to the five best candidates for it is also the combination of the keywords that gives a specific context.
What role does bias play in this?
Unfortunately, there is still a large margin of error in this model. The job description belongs to the client, who sets specific requirements for the applicant. We let go of this model and set the AI to choose words that are most prominent, but this is not without its flaws. For example, imagine that it says ‘assistant to the CTO’. The AI is not developed enough yet to analyze this properly, so it will only look for CTOs.
Are there any other problems you face as a recruitment company?
It is great that we can work with software that saves us a lot of time and which can support us with the analysis of lists. What we often see is that the speed at which the demand for data talent is growing is growing faster than the Dutch market offers. Many Dutch organizations will have to get used to the fact that they do not always have a Dutch citizen in the office. Companies must understand that they also need to work with more remote talent, and they get scared. In the flanks of Europe, there is the talent that is often more attractively priced than the talent that is here.
One of our most substantial growing business models is recruitment and selection. If you want to increase your company and decide that your staff can be remote, instead of having a ‘recruitment and selection’ fee, we invoice per month. That often feels a little lighter for a young company. We can then deliver quite an attractive talent without immediately having to pay a hefty fee.
How do you find the right Tech talent for the right company?
It’s hard to let an AI do this. The manual work starts with downsizing a long list into a shortlist. Specific wishes of both clients and candidates then come into play. Every candidate is attractive because of a highly competitive market, and every company has deep-rooted wishes that cannot always be met. We then see what weighs heavier on both sides, something that cannot be extracted from a text.
Occasionally we come across a candidate who makes impossible demands because this person knows that he/she is wanted. There are tech companies with enormous financial opportunities that go along with the significant demands of the candidate. These are no longer financially defensible, but the concessions are made. These are salaries that the average person can only dream of. Our role in this is that we must manage expectations and be realistic towards both the companies and the candidates.
How do you find the right companies and partners to work with?
We excel in those ‘more difficult positions’ where competition is high. We, therefore, look at companies that have a challenge with fulfilling their position. A large company like Google that pays a lot doesn’t need us. We know how to find younger or smaller companies that have more difficulty finding the right talent.
We work with three different profiles: hackers, hipsters, and hustlers. The hacker can program well. The hipster can understand the concept of the company. The hustler can often arrange business to business. These three are necessary to set up a good software company.
How can the AI community help Tech Rise?
Very simple: We are looking for parties with whom we can work. We are now looking for clients who like to expand their teams and who are open to external support.
