The Hex of Complex

Why data startups are struggling to find Go-To-Market talent

In 2022, data startups made serious headway. Technological advances in AI and machine learning have created new possibilities for enterprise customers hungry for automation and insights. MLOps planted its flag within technical departments as companies continue their effort to push ML models into production. Generative AI pushed its way into the spotlight. Notable venture capital firms like Insight Partners, Redpoint Ventures, Benchmark, and others have been deploying capital into the space. Brilliant minds from academia have devoted their brain power to initiatives in AI, Machine Learning and Data Science. And innovation is happening at an exponential rate. 

But despite all the IQ, the technical advances, and the growing amount of venture funding pouring into the sector – a challenge remains.

Companies in this space seem to be pulling their hair out trying to find, hire, and ramp GTM talent. And it’s not just sales. We’re talking to leaders experiencing these challenges in Solutions, Customer Success, and even Marketing. We started to look closely at the problem to find out why this was happening.

Over the course of the last year we’ve gotten an opportunity to work with some of these data companies first-hand to help them build their teams. We’ve also had the chance to speak to many others to learn about what they’re going through. And what we found out makes perfect sense.

The obstacle

The challenge lies in the complexity of the tech.

Leaders are telling us that team members that don’t have prior exposure to the intricacies of the sector are struggling to understand key concepts, and subsequently are taking much longer to be effective in their roles. This creates a recruiting challenge for Go-To-Market leaders in Data that is somewhat unique. 

Take an HR-tech company as an example. If they sell an HRIS software to enterprise businesses the key persona they sell into is probably a VP of HR, a CHRO, or something of the sort. The business challenges they solve probably circle around things like payroll, benefits, talent acquisition, employee engagement, etc. The HR-specific jargon exists, but is elementary by comparison. The concepts can be learned relatively quickly. And while having some prior exposure to these concepts could be helpful, hiring an industry outsider with no experience in the space is more than plausible. 

Conversely, in the high tech world of data/AI/ML the learning curve can be excruciating. This creates a clear business problem for these companies when it comes to hiring not just for engineering, but also their customer-facing business units.

In sales, this type of technical knowledge gap means an Account Executive might need twice as long to be fully ramped. Played out on a company-wide scale CAC payback suffers, sales leaders miss quota, promises to the board/investors are walked back. In solutions/presales, bottlenecks emerge as more senior members of the department are forced to take on the majority of the technical load, In marketing, messaging misses the mark for key prospects and pipeline dries up. In Customer Success, the backlog lengthens and customers leave feeling unheard.

The key in all these functions at any company is to intimately understand the customer/prospect, their business drivers and pain points, and your product. If you can synthesize this understanding into a value proposition, you can be effective. 

Even the best GTM talent that does not have a knowledge of the space, is likely to struggle. And leaders looking to hire candidates that have the best chance of delivering quickly are staring at a very small target.  Not to mention, in spite of the recent economic headwinds, the labor market remains much tighter than pre-pandemic levels.

What’s not working?

Unfortunately, fishing with job boards is only marginally effective. The word is out regarding the opportunity in this space attracting swaths of unqualified candidates. Recruiters and hiring managers know all too well the pain of combing through dozens, or even hundreds, of profiles only to find a select few that have the necessary skills. The return on time spent is minimal. We firmly believe that recruiting, at its best, is an outbound motion. 

The old boolean keyword search is better, but still lacking. The trick is finding candidates that have relevant technical experience. Not so easy in this case. Unlike developers, GTM talent is much less likely to have a glossary of technical terms they have experience with on their resume or linkedin. This can be stifling to internal and agency recruiters alike.

What’s the fix?

While finite, the first and best choice is always to lean on your network. A more systematic approach we’ve found is to properly map the market. This is a heavy lift to say the least, especially considering the rate of innovation in the space. Elaborate tagging, data scraping, research, and more research. The key is to be able to back into the right pool of candidates through the companies they work for. Knowing which companies have relevant products will give you an idea of the candidate’s technical proficiencies. Building a market map internally will give you an edge in the talent market.

“I don’t have the time.”

A project like this, done correctly, can certainly be an investment. However, the ROI can be enjoyed for years as the company grows. Perhaps there are shared resources internally that can devote time to breathing life into a project like this. But keep in mind, just like any network the data set is dynamic. This requires on-going attention to stay up-to-date and effective.

“I can’t get this done internally.”

You’re not alone. Startups rarely have spare resources that can move to action on projects outside of their core business objectives. Of course you can hire a recruiter. But in this space, generalists deliver sub-par results. Choose wisely.

Recruit the Future.

Earlier in the year we decided to start mapping out this market extensively. We’ve carved out 1,906 companies across Data Science, Analytics, AI and ML. With well over 20,000 rows of data, our map has become the fuel of our recruiting engine. With a few clicks we comb through a massive bank of US-based GTM talent in this sector and can quickly identify candidates with relevant technical skills that otherwise would have gone unnoticed. And, with every search we run the dataset gets stronger. Want to see it? Reach out to graham@msearchco.com we’d be happy to show off our hard work.

2023 will be another year of acceleration for select startups that are able to hold investor interest and grow revenue. Even so, many candidates seem to be considering larger organizations with the thought that bigger means more stable. And while the numbers refute that to some degree, it remains another hurdle for startup leadership to overcome. As the space broadens and new entrants battle for market share, so too will they be looking to lay claim to the top GTM professionals with that prized technical knowledge of data/AI/ML. It will be important to develop a recruitment strategy that gives you a repeatable system for finding qualified profiles. This coupled with strong messaging, a tight recruitment process, and a competitive offer will give you an advantage the next time you need to hire.