How to hire better Go To Market Talent for AI by using Data Science [Guest]
A look at how Data Driven Decision making plays out in non-tech scenarios
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Recently I spoke with Graham Locklear, the CEO of M Search- a talent agency specializing in placing high-quality GTM talent for AI companies about the possibility of us working together. Towards the end of our conversation, Graham mentioned their unique hiring process, which he was kind enough to show me. Graham has graciously agreed to do a guest post over here- where he will break down how his company uses Data to refine their candidate search in a competitive space (Graham shares an IRL Case Study, which was super cool).
I’m particularly excited to invite Graham here for two major reasons:
The simplicity: If you are into heavy data analysis, the way M Search uses Data is offensively dumb. That’s a good thing. Difficult Data Science is not Effective Data Science. M Search uses just enough to fish out the right candidate, without wasting energy in boiling the ocean. While the cutting edge gets most of the attention, people underestimate how much of the heavy lifting is done by simple setups, which pays the bills for the fancier setups.
A different perspective: Given the nature of my writing- most people I attract have a heavy technical orientation. This includes all the guests we’ve had so far. While I will not go away from that, I don’t want this newsletter to become too myopic. I want AI Made Simple to be less about AI/Data as a technology and more about the people that use these to solve their problems (even if they tried and failed). This article is a great insight into the more commercial side of things (on a tangent- I also really liked the DALLE prompts used for the images here- they made me smile).
If you have any insights you want to share, I’d be happy to have you on. It doesn’t have to be technical (or even AI-Related). One of the perks about writing for an established audience is that I get to talk to and learn from the journeys of various people. And it goes without saying, but I’ll be sure to have enough Technical Detail to keep you happy <3.
Now onto Graham’s writeup
PS: Based on the last poll, there was a strong interest in an AI Made Simple meetup group in NYC. If any of you have affiliations with Colombia, NYU, or any of the other NY Schools, please get back to me. That way, we can get the rooms arranged. If you know any alternative hosting spaces (aside from the schools)- I’d be down to discuss it with you as well.
The Hex of Complex
Why data startups are struggling to find Go-To-Market talent
2023 has been another impressive year for all things data + AI/ML. Technological advances in AI and machine learning have created new possibilities for enterprise customers hungry for automation and insights. MLOps, having cemented itself as a required layer of the stack needed to deploy models, spins out a cousin of sorts: LLMOps - specifically to manage large language models for GenAI initiatives. Generative AI is still the talk of the town, or at least the talk of every Fortune 500 board room. Virtually every top tier VC has been placing bets on AI. 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.
Go-To-Market, or GTM, covers all things customer-facing and commercial related. Think: Sales, Customer Success, Solutions, Marketing, Implementation, and Account Management. In 2021, we started noticing a trend among Data + AI/ML companies - they were consistently struggling to find the right types of hires in these departments.
We started to look closely at the problem to find out why this was happening.
Over the course of the last 2 years, 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.
Normally, for GTM hires technical skills are sort of an afterthought. But leaders of these departments within the data/AI/ML sector are telling us that team members that don’t have prior exposure to the intricacies of the technology 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 (which is twice as long until they can be expected to start hitting sales quotas). Played out on a company-wide scale CAC payback (the amount of time it takes a company to recoup the cost of getting a new customer) suffers, sales leaders miss quota, promises to the board/investors are walked back. In solutions/pre-sales, 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 space, is likely to struggle due to the complexity of the underlying tech. 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 can give you an edge in the talent market.
Recruit the Future.
Towards the end of 2021, we decided to start mapping out this market extensively. We’ve carved out over 2100+ companies across Data Science, Business Intelligence, 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 (and getting stronger in UK+EMEA) 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.
Let me walk you through a recent search to show how our market map is able to deliver.
Our Process in Action
In this case, our client is a Fortune 500 tech company. The company had acquired 2 MLOps startups and merged them with a proprietary LLM training platform with the goal selling into enterprise customers looking to build LLMs and internally leverage GenerativeAI.
As you can imagine, the sale is very technically involved. The buying persona reaches from Data Science and ML teams through business units, IT, Finance, and ultimate to the executive boards of these companies. But the only people within customer orgs that know enough to be an informed buyer are the technical people within the org. Think Chief Data Scientist, etc.
As such, the profile we needed to find to fill this role had to have experience selling to similar personas. They need to have enough time in the subject matter (specifically ML and Deep Learning) to have a handle of the language. They needed to find enterprise sellers with the right technical acumen.
The first thing we did was to consult our market map to refine our search for ML centric product sets. Think MLOps, Data Sci All-in-one platforms like Dataiku etc. We even opened up the search to some CV data labeling/annotation tools etc.
This gave a list of around 215 companies. From there we filtered down the list by sorting US Headcount. And began sourcing. Because we have done the research beforehand, we know that if there is an Enterprise Account Executive at one of these companies, they almost certainly can talk the talk when it comes to our clients tech.
We use a qualification framework that we call F.A.C.T.. It stands for Functional, Achievement, Customer-set, and Technical/Product-set. With the ‘T’ out the way, we were able to apply the other criteria across our cohort.
When our research was done,we came away with 378 candidates that met our criteria on paper. We were able to generate 43 screening calls from this group, and ultimately submitted 8 candidates. Our client met with all 8 and hired 1.
We collect plenty of metrics on each search that gives us insights about the target market.
For example: on this search we had a 46% engagement rate (candidates that responded in any way to our outreach). That’s lower than the average engagement we see (closer to 60%), which tells us that this group is probably doing pretty well in their current roles, their companies are performing decently, and they are most likely heavily recruited.
We also track initial interest rates. On this search we saw 38% initial interest to our outreach. So, of the people that responded, 38% were interested in learning more. This is a tad lower than normal, but not enough to glean real insights other than confirming the initial hypothesis that this group is doing well, and generally getting a lot of calls. Its a competitive talent sector.
The percentage of candidates that agreed to a screening call after learning more was actually higher than average - 63% vs an average of closer to 50%. This tells us that our messaging was resonating, and the client’s opportunity is an attractive one to the target market.
Of the candidate we screened the overwhelming majority did have the right technical experience. The ones that were not submitted to the client were mostly dispositioned due to cultural or performance related factors. So we know our research was on the money!
By constantly improving our market map and tracking the activities across each search we can ensure that a) we are spending our time engaging candidates who have a high likelihood of being qualified, and b) our clients can feel confident that they are only meeting with the top percentile of the target market. The activity tracking also helps act as an early warning system for us. If we notice engagement rates are trending much lower, we can discuss with the client and course-correct before too much time is wasted.
DIY
“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 your 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.
Looking ahead
2024 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.
If you're interested in hearing more about how we are using data to find companies the right hires you can follow me on linkedin: https://www.linkedin.com/in/graham-locklear/ OR email me at graham@msearchco.com.
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Finding an article like this today is both encouraging and disheartening. Now two months since my company folded, I'm trying to parlay broad and nuanced perspectives on data and marketing and their impact on society, along with technical skills, into a new position with the right org.
Five interviews deep into a marketing tech company, I was offered a position as a data engineer. When I sent a counter offer suggesting my 'soft' skills could further enhance my value proposition, they withdrew the offer entirely, saying their organization "couldn't support someone with [my] impressive qualifications."
I know there is a broad need for the combination of technical skill and visionary humanism. I'm getting used to seeing it as a deeply personal challenge, but this call to think of it as an HR challenge that can be eased with incisive use of data was illuminating. Thank you.