Engineers Working in Office Together

Building Incredible Remote AI and Data Science Teams


In 2019, Stripe announced that their fifth engineering hub would be remote because they wanted to tap the 99.74% of talented engineers living outside the metro areas of San Francisco, Seattle, Dublin, and Singapore (their first four physical tech hubs). This decision shines a light on how inefficient, and somewhat irrational, it is to spend several months trying to hire local engineers, only to have these engineers poached by competitors a few months later.

After all, in this day and age, when the world enjoys the highest levels of technical talent distribution and real-time communication in human history, what is the probability that the absolute best engineers for a given project are conveniently located within commuting distance of our offices?

The Move to Remote Engineering and Distributed Teams

Stripe joined the ranks of many top tech firms expanding beyond a few traditional tech hubs and embracing distributed engineering teams. Typically, only the largest global tech firms like Google, Facebook, and Amazon were able to establish multiple global locations to reach new talent in other geographies.

More recently, however, an increasing number of companies, like Zapier and Stripe, have remote engineering teams as primary solutions without the need for heavy investments in physical offices. Not to mention the COVID-19 pandemic making it clear that working from an office is no longer necessary.

Engineers in a Meeting

It’s Difficult and Expensive to Hire Local Engineers

In traditional tech hubs like Silicon Valley, Seattle, New York, and Austin, competition for engineers is incredibly fierce. According to studies published by IBM and LinkedIn, data scientists and machine learning engineers are the fastest growing and most difficult roles to fill. It takes the average company about 5 to 6 months to fill a data science role, and some of these openings remain unfilled for over a year. 

Complicating matters further is that qualified engineering talent regularly gets poached by FAANG companies (Facebook, Amazon, Apple, Netflix, Google), so the vast majority of talented engineers typically don’t consider job openings in other companies.

In response to these growing difficulties, building a distributed team of remote data analysts and engineers is a smart strategy. If done correctly, building a remote team can significantly reduce cost and delays in building a highly qualified engineering team.

High Quality Analytics and Engineering Talent Abroad

It’s widely accepted that raw intelligence and tech talent is broadly distributed around the world, so there are plenty of top engineers living outside of Silicon Valley and traditional tech hubs. Those of us who’ve had the opportunity to live or study abroad won’t be surprised to learn that schools and universities outside of traditional USA tech hubs are often more rigorous when training local mathematicians and engineers.

Particularly for quantitatively focused roles in data analytics, machine learning, and data engineering, the rigorous training that students receive abroad in mathematics, statistics, and engineering creates a healthy pool of highly qualified “quants” around the world.

Vetting and Testing Remote Engineers 

Finding great engineers remotely, at scale, requires building bonds with the local tech community and universities, as well as implementing a rigorous process for vetting and testing these engineers. Not only are you looking for great technical talent, but you’re also looking for superior communication skills and personality traits that will make for effective remote contributors.

At Factored, our recruiting process is extremely rigorous. We accept less than 3% of applicants ( a lower acceptance rate than Harvard or Stanford). We use multiple coding interviews, cultural fit interviews, and programming and theory tests to ensure proper vetting of candidates.

Tested against thousands of other engineers around the world, our engineers rank in the top 1-3% in core skills like mathematics, data science, machine learning, deep learning, and algorithmic programming. This rigorous vetting process guarantees that our engineers are Silicon Valley ready.

Factored Team Together

Working in the Same Time Zone is More Productive 

One important consideration is the issue of time zones. At Factored, we’ve found that while physical proximity is not as important, real-time collaboration and feedback is still essential. 

Though some companies argue that asynchronous communication is satisfactory because it allows coders to focus on coding without much distraction, at Factored we’ve found that the solution is having distributed teams of remote engineers who live and work in USA time zones.

Too many engineering managers have dealt with time zone difficulties when working with remote offshore service providers. The idea that “they work while you sleep” sounds great in principle, but it leads to wasted time and resources due to shortened communication windows. Nearshoring models, where engineers “work while you work” are much more effective. We believe this real-time presence is essential to making remote engineering productive.

Not to mention the shockingly long commute times in places like the San Francisco Bay Area or Silicon Valley, where some super commuters spend 3 hours daily getting to and from their office. 

Beyond the terrible effects on the environment posed by such long commutes, think about the wasted time and productivity these long commutes engender. That’s an average of 225 extra hours of productive work that your team could spend solving business problems or building products and valuable IP instead of sitting in traffic.

Engineer working at desk with computer

Minimizing Legal and HR Complexities 

There are a number of other potential hurdles to consider if you try to build remote offices and structure remote employment yourself directly. If you’re a growing tech company, you probably want to focus on building your product.

You’re probably not in the business of learning about and complying with local regulations related to employment law, payroll, employment, taxes, healthcare, and market-competitive benefits. This could add significant costs and complexity if you decide to tackle this directly, which is why we recommend working with experienced third-party companies who specialize in this area.

They will save you headaches, time, and money. Factored, for example, handles all of these requirements when building data science and AI engineering teams for our client companies.

Don’t Wait, Start Building Your High Caliber Data Science and AI Team Today

It makes no sense to keep your data analytics, engineering and machine learning projects on hold because of hiring difficulties. Most leading tech companies are already tapping the superior data analytics engineering talent outside of their local areas and are building a competitive advantage much faster and more cost-effectively.

By implementing a rigorous vetting process, and building the right partnerships, your company could start placing great engineers on your projects in a matter of days.

If you’re looking to build a team of top data analysts, data engineers or machine learning engineers, get in touch:

Related Posts