Why You Should Look Beyond Silicon Valley for Data Science Talent

Why You Should Look Beyond Silicon Valley for Data Science Talent

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Factored Data Experts

The Great Resignation has contributed to what was already a shortage in AI and data science talent. As we mentioned in a previous post, studies published by IBM and LinkedIn show that machine learning engineers and data scientists are the fastest growing and most difficult roles to fill. In fact, it takes the average company about five to six months to fill a data science role, and some of these openings remain unfilled for over a year.

Here are some telling statistics on the present state of global AI and data science—including the workforce such initiatives will require—from around the web:

  • Gartner: “By the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures.”
  • McKinsey: “Many executives now realize that AI solutions typically need to be developed or adapted in close collaboration with business users to address real business needs and enable adoption, scale, and real value creation. As a result, we see companies increasingly developing a bench of AI talent and launching training programs to raise the overall analytics acumen across their organizations.”
  • Deloitte: “In 2020, for the second time in four years, the number of jobs posted by tech companies for analysis skills—including machine learning (ML), data science, data engineering, and visualization—surpassed traditional skills such as engineering, customer support, marketing and PR, and administration.”

Business Problems Caused by the Talent Shortage

The dearth of AI and data science talent is damaging for business growth. As IoT and AI technologies generate more and more data, that data doesn’t mean much if companies don’t know how to use it effectively. It also means that great ideas often have to be put on the back burner.

This can result not only in lost revenue, but higher-risk decision making, missed opportunities, and even a loss of reputation.

This is exactly why “AI high performers” according to McKinsey, typically employ more AI-related talent—such as data architects, data engineers, and translators—than their counterparts. But how can you find such talent in today’s hiring environment?

Well, you could go the traditional route—which is super competitive and time-consuming—or you could go the non-traditional (and, in our eyes, better) route. Let’s take a closer look at each.

Team sat on sofa together talking

How to Find Talent

The Traditional Route

Companies often look for talent that lives within the general vicinity of their offices. This not only shrinks an already tiny talent pool, but also means they might not be hiring the best person for the job. Additionally, if—after spending lots of time and money on the talent search—you are able to find someone, you have to make sure that person stays happy, as qualified engineering talent regularly gets poached by FAANG companies (Facebook, Amazon, Apple, Netflix, and Google).

The Non-Traditional Route

According to our co-founder, Andrew Ng, there simply isn’t enough AI talent in Silicon Valley or even the US. That’s why building a distributed team of remote data analysts and engineers is the way to go, as it can significantly reduce costs and delays in building a highly-qualified engineering team.

Think about it this way: Raw intelligence and tech talent is broadly distributed around the world; this means there are plenty of top engineers living outside of Silicon Valley and traditional tech hubs.

LATAM, for example, is experiencing impressive tech growth, with a burgeoning startup and funding ecosystem. This makes it a perfect place to find excellent tech talent, whether you’re looking for data engineers, machine learning engineers, or data analysts.

This nearshoring solution means talent is able to work the same hours as US time zones, helping US-based companies advance new initiatives and AI capabilities with real-time collaboration and feedback.
This is in contrast to traditional outsourcing, when different time zones can often mean shortened communication windows and adversely affected workflows, efficiency, and productivity. (We go more in-depth into the pros and cons of nearshoring vs. outsourcing in this post.)

Find Top Data Science Talent Today

Looking for tech talent uniquely in Silicon Valley—or even the US, for that matter—isn’t sustainable, ideal, or necessary. Especially in today’s remote-first world, it’s all about ensuring that employees are intelligent, ambitious, and hard-working—not that they live within the vicinity of a company’s headquarters.

Factored is a bi-cultural team that has a deep understanding of and connections to both the US and LATAM in terms of education, business, work culture, and how to build AI businesses. Our talent network has worked in various industries, from fintech and marketing optimization, to supply chain optimization, retail, and more. Our rich set of expertise—not to mention our passion and belief in our mission—can help you build out your tech teams with superior LATAM-based talent.

Don’t let your AI and tech initiatives wait on the shelf collecting dust. Build your company’s competitive advantage faster and more cost-effectively. Use Factored to get started on your projects quickly with the support of high-caliber engineers.

Are you ready to learn how Factored can help? Book a meeting with us to discover how our engineers and analysts can help you solve your hiring needs.

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