The Unique Challenges Startups Face Hiring Data Scientists
Despite the global disruption and a new normal for working conditions everywhere, interest in startups is still going strong. Deal flow might be happening at a slower pace, but in the US at least, funding hasn’t dried up. It might be slower than in previous years, but capital is still flowing. That’s good news for startups and even better news for data scientists. As these companies navigate the post-COVID landscape, the need to become data-driven isn’t a luxury anymore. Now, hiring data scientists s a requirement for survival.
A Thirst For Technical Talent
While some of the hottest startups “do data” as their primary function, plenty of other wildly popular startups have non-technical founders. The need for data-rich insights is only growing, leading startups to compete with big tech for the best and brightest.
Startups without tech talent have no recourse but to outsource their technical development while they pursue an in-house team. This may have brought us such ubiquitous startups like Slack and WhatsApp to the mainstream, but for many startups, that means handing over “the keys to the kingdom.”
The Problem With Outsourcing Tech
There are so many out-of-the-box solutions for startups and enterprises alike to handle their data, but the foundation of technical expertise must remain in-house. Without that expertise, startups run into a myriad of problems, even building their in-house teams.
- Vetting talent — Without at least one expert on the in-house team, it becomes difficult for C-suite and project managers to vet talent. Someone must have the technical experience to conduct reliable interviews and ensure a good fit with the overall data strategy.
- Competing with big names — It’s a vicious cycle. Small companies outsource their tech while big companies scoop up talent. Talent doesn’t want to come into fix or build solutions for a small company; they want to work on cutting edge projects with established names, so they flow there. Rinse and repeat.
- Global disruptions slow the appetite for risk — Startups are risky. Without guaranteed revenue streams or even a guaranteed job in three to five years, candidates are making “safe” decisions going with large companies unlikely to go bottom-up. With so many jobs already affected, workers could be staying put.
Still, startups are finding data and AI fundamental to their operations, and these challenges only compound the issue. Overcoming the tech shortage in talent searches will prove a vital part of a startup’s survival in the coming years.
Sourcing Data Talent and Hiring Data Scientists
There are a few ways startups are sourcing their talent outside the traditional job posting. LinkedIn offers greater access to senior tech talent through networking, unlike the basic talent sometimes found through Indeed or mid-level at Angel List.
In many cases, startups rely on word of mouth, connecting each other to talent interested in the problem they’re solving or the type of project they’re working on. This is one of the best ways for non-tech startups to vet talent, but it’s often inefficient. They happen on their perfect candidate… or no one has any connection.
Data science is a necessary component of so many startup solutions, so the need is still there. Why then is it so difficult to connect the two? Even traditional studies linking FAANG to greater job satisfaction aren’t as straightforward as they seem on the surface. Yet there’s still a talent shortage out there.
A few factors could explain the gap:
- Startups are trying to compete on FAANG terms with salary and equity instead of offering their own unique benefits.
- Startups cannot properly vet the senior talent they need to build a solution from the ground up. Exams at Upwork are only for the junior level, for example, and startups have no one on the team with the expertise.
- Startups are waiting for the perfect candidates to come to them instead of aggressively courting talent.
- Startups are waiting for unicorn talent when the perfect candidate doesn’t actually exist.
What GetAIPlus.com Can Do for Hiring Data Scientists
Our newest solution can help startups solve these problems by changing the direction of outsourcing and hiring data scientists. Instead of taking the dangerous route of outsourcing tech talent itself, companies can outsource their hiring (to an extent).
The purpose of getaiplus.com is to provide domain-level expertise startups need to find, vet, and hire top talent without the tech side’s knowledge. Not only do we have the network of talent waiting to connect with startups and companies alike — we have the expertise to evaluate their skills.
We’re able to take on your hiring — from individual hires to constructing entire teams — and build your tech talent solution with the knowledge and understanding of tech experts. You get talent you can rely on and break the cycle of tech outsourcing.
Take a look through our new site today to solve your tech talent issue and compete with big names for the experts you need. It’s time to become data-driven once and for all.
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