WE HAVE TO TEACH SPECIALISATION, WE CAN’T EXPECT IT: A Q&A WITH VIN VASHISHTA

Ross Whatling our consultant managing the role
Posting date: 4/23/2020 10:08 AM



We recently spoke to Vin Vashishta, a consulting Data Scientist and Strategist who was named one of LinkedIn’s Top Voices in Data Science. 


Having started off in the tech world 25 years ago and progressing from web design and hardware installation to Business Intelligence Analytics, Vin found for many years that enterprises were reluctant to adopt AI technologies and embrace the value of Data. In fact, it wasn’t until the beginning of the decade just passed that companies started to think about their Data more strategically and the world of Data Science was born, albeit hesitantly: 

“When I first started, it was a lot of experimentation, everyone wanted a proof of concept,” he says. “A lot of work was creating models that could go from whiteboard to production and productise and show their value.”

However, it wasn’t until halfway through the decade that he began to see businesses who had adopted Machine Learning move away from experimentation into incorporating it more deeply into their companies, relying more on analytical and optimisation models to make strategic business decisions. 

“After that, in about 2017/2018 the maturity changed. It went from being a one off implementation to it being a comprehensive tool within an organisation where we have full lifecycles of model implementation and full models that were full views of the system. The key component of development was allowing users to access a small part of the system to do their job better without having to understand the whole thing. And that’s where we are now. We have this applied Deep Learning and we are seeing, especially this year, attempts to optimise that, make things go faster and make them more repeatable.”

But, as we all know, with great power comes great responsibility: “There’s this whole depth we are getting into, the expectations are so much higher, people don’t just expect it to work they expect it to work the way they want it to and in a way they can adopt.”

So, with so much expected and required of Data Scientists in 2020, building the right team is more important than ever. However, many businesses, Vin believes, are yet to get their hiring processes right:

“A lot of the measures that we use to sort of evaluate employees are fictional – when you say years of experience, it has no correlation to employee outcomes or the quality of employee you get long term. It’s the same thing as college degree, there’s no correlation.”

So when Vin is trying to build a highly specialised team, what does he do?

“We have to teach specialisation, we can’t expect it. We can’t bring someone in and call them a Data Scientist and hope that they train up. You end up with teams that are exactly the same because they have hired the same people, people who reinforce the bias of what they do, and that is where true leadership needs to come in.”

A specialised team made up of individuals who bring their own ideas to the table is more important than ever, particularly as businesses demand more from their Data teams. Gone are the days of one-size-fits-all models. Businesses now want something tailored to them:

“Custom models are huge. The “import from…” Machine Learning development from three years ago adds value when it comes to wrangling and doing the Analysis, but when it comes to creating models companies are now expecting it to become a competitive advantage. Companies no longer want the same model that everyone else has, now it has to be differentiating.”

These smart, customised models, he adds, will help businesses through the current pandemic. “The best models right now are adapting rather than reacting.” 

However, he’s sceptical about the Data Science community becoming too preachy: 

“When it comes to COVID-19 one message I want to send to the Machine Learning and Deep Learning community is ‘shut up’. We don’t have the Data! We have so many Data Scientists talking about something that’s very important to get right. If you get it wrong the consequences and the credibility we will lose as a field is enormous.”

Indeed, discussions about the lack of quality Data on COVID-19 are widespread at the moment and raise concerns for Vin:

“What the last two and a half months has revealed is the danger of bad Data, the danger of assumptions that are hidden in Data that hasn’t been looked over well or wasn’t gathered well and was fed into these models that now aren’t robust. Of course, no model can account for something this drastic, but they should still be performing far better than they are right now.”

Despite these concerns, Vin believes any change in the world brings about opportunities for those in the Data and technology space.

“What I’ve been trying to do ever since I joined the technology space is figure it out. It’s constantly evolving and it’s constantly changing. That’s really what has driven my journey. I’m always trying to figure out ‘what’s next’ over the next five years, ten years whatever it may be.”

If you’re looking for your next Data Science, Machine Learning or Deep Learning role, or want to build out your own highly-specialised team, we may be able to help. 

Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.   

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