How can startups hire the first set of data scientists? In conversation with Bob Friday - VP / CTO at Juniper.

Successfully hiring the first set of data scientists at a startup

More companies are recognizing that they cannot build and leverage in-house artificial intelligence/machine learning models from the very first month of setting up a data science team. With the increase in the influx of venture capital money, global startup hubs are seeing more data science jobs being posted and increasing demand for AI and data science experts.


It is said that when it comes to building a data science team, many companies fail at the first step — creating a job posting. However, it doesn’t imply that the challenge ends there. It can be confusing to hire the first set of data scientists, and hiring managers feel overwhelmed with a lot of opinionated content for “data science hiring,” while the job description of a data scientist may be different from company to company.


While we wrote a blog on how to hire data scientists at a company, we considered asking our friend and one of the world’s best minds in the field about this. 


During one of our recent episodes of Hiring Diaries, we hosted Bob Friday – VP / CTO of Juniper Networks. Besides that, he is a serial technology entrepreneur. Through his work in wireless self-learning networks, his company has achieved a global Gartner Magic Quadrant Leader status in just six years. Bob’s first tech startup went for an IPO, second technology startup got acquired by Cisco for $450 million after which Bob joined the organization as its VP / CTO. And he’s still leading his third startup in the capacity of a Co-founder and CTO of Mist AI – now a Juniper Networks company. 


Mudit Srivastava, Host,  

If you look at the industry at this point, hiring data scientists is a significant pain point. Every day, we see 1000s of companies posting data science job openings on LinkedIn. When you look at the number of positions filled, it appears that the picture is very skewed. Plus, seeing from the viewpoint of startups, the problem at their level is different than that of an enterprise. In what ways has the hiring of data scientists changed – from the time you started Mist AI until what it is now? 


Bob Friday, Mist AI and Juniper Networks:  

I started Mist in 2014. You know, the first data scientist I ever hired was a particle physicist. You see, from 2014 to 2020, hiring data scientists has become more challenging and more expensive. I would say, in a startup, it’s even more complex and I think there are two models for hiring data scientists. 


One, if I’m in a startup, you have to be a data scientist, and you also have to be a sound software engineer. Because what I found in working with data scientists is they don’t have the domain expertise. You’re trying to hire someone who understands very detailed statistics and math and can write software. And that’s usually a scarce combination. 


And the big companies kind of get away with hiring Ph.D. data scientists that don’t know how to do production software. They know how they may see the math and the models, but they really can’t write software that you can deploy in production. When you’re in a startup, you have to find people who can do both. 


I don’t have the luxury of having pure Ph.D. data scientists who don’t know how to write code. And I would say my first experience was hiring a particle physicist, because they tend to understand all the math if you look at physicists, and I would say, that was kind of the what I looked at hiring between the cracks, you look for people who understand the math, it’s easier to find someone who understands the math and teach them how to write software, than trying to find someone who writes software and teach them the theory of the math. So that’s kind of how I got started in trying to find my first couple of data scientists to help me lay the foundation at Mist.


Mudit Srivastava, Host,  

Okay, that is a recommendation you will give to anyone starting up an AI startup on today’s date? I mean, because it’s something that we come across regularly. They want to hire the first one or two data scientists, and they’re worried about it for a few months. So, yeah, what’s your recommendation on that?


Bob Friday, Mist AI and Juniper Networks:  

Nowadays, if I’m starting a new company, we’re competing with Google and Facebook. Someone has to choose between wanting to start a startup adventure, these data scientists, they can go to Google, Facebook, Apple, and they can get massive, they can get enormous W-2s (or call it “salaries” if you’re referring to a region outside the United States). Ttrying to convince someone that they should join a startup versus going to Google is kind of hard. Especially if someone’s just out of school. 


So usually I look for people who want to learn and I go to a lot of colleges and do similar speeches, like if you’re coming out of school, you want to go down the startup path, or the big company, W-2 path, you know. Usually, I tell people that, hey, if you can, if you can survive in a startup environment, and not everyone can, because usually, startup environments are very chaotic. And if you’re not a self-starter, you have to be able to jump in, do I call it the deep end of the swimming pool and get yourself started, then you will learn more in a startup environment, than in a big company environment. You know, so I typically look for people who are more interested in learning than being in big W-2s.


Mudit Srivastava, Host,  

Okay. But at times, what I have seen is, when somebody is just starting up and it’s a common problem that I’ve seen is that they typically tend to look for someone with some experience in the industry. What’s your take on this? Do you think somebody should go and just start with university grads who are brilliant and are willing to work with you, or are you of the opinion that you should have someone with a lot of experience who can mentor these people?


Bob Friday, Mist AI and Juniper Networks:  

Experience is good. But I prefer to have a high IQ. So usually, I choose to find someone who understands math. Two, that they know the theory behind all these algorithms to some extent, that knows how to write software. And three, they’re passionate about producing a product because usually, I found when you’re working with data scientists, there are certain data scientists who are more into the research they’re not interested in actually making a product work. They’re more interested in the theory behind something, which is fine. 


But when you’re in a startup or a company, right we’re actually in the business of producing products. And so, what I found is usually data scientists, usually, I find PhDs who’ve been researching for a couple of years. And they’re finally tired of writing papers. They’ve been writing papers for three, four, or five years. And they finally decided they only wanted to see if they could build a product. You know, so that tends to be one pattern I’ve seen in hiring data scientists. They’ve done a lot of research, but now they want to try to see that you build a product.


Liked the conversation?


This is an excerpt from a recent episode of Hiring Diaries with Bob Friday, VP / CTO of Juniper Networks and Co-founder of Mist AI – a Garner MQ Leader. Our entire conversation covered several topics, including:


  • In Bob’s journey of doing an IPO and two big company acquisitions, how has raising money changed over the last 20 years?

  • Theory and the product behind Mist AI.

  • Ground reality of AI implementation. Is AI overhyped?

  • Hiring the first set of data scientists at an organization.

  • What should be the difference in approach while hiring data scientists at startups and versus an enterprise?

  • Insight into AIOps and its recommended hiring practices.

  • Parameters to look for while hiring data scientists and MLOps professionals.

  • Key levers that took Mist from a blank sheet of paper to the Gartner Magic Quadrant leader in just six years?

  • Starting up a tech venture in today’s world.


To view the entire discussion, visit here –


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