How This Engineer Pulled Off a Career Pivot From Marketing Into AI


  • The rise of artificial intelligence has prompted a boom in demand for machine-learning expertise.
  • Ivan Lobov, an engineer at DeepMind, worked in marketing before pivoting to AI.
  • Insider sat down with Lobov to find out how he pulled off the career pivot.

As more industries find innovative ways to apply artificial intelligence to their goods and services, companies want to staff up with experts in machine learning — fast.

Recruiters, consultants, and engineers recently told Insider that businesses face a shortage of machine-learning skills as sectors like healthcare, finance, and agriculture artificial intelligence. Banks, for example, rely on AI to aid in fraud detection.

Machine learning, among the most commonly used forms of AI, allows computers to extract patterns from huge amounts of data, making it useful in a variety of fields.

Ivan Lobov is a machine-learning engineer at DeepMind, the AI ​​research lab owned by Google. Back in 2012 he was working in marketing at Initiative, an advertising agency that’s put together campaigns for brands such as Nintendo, Unilever, and Lego.

DeepMind engineer Ivan Lobov began his career in marketing

Lobov, now a DeepMind engineer, began his career in marketing.

DeepMind


“My job was to make presentations and pitches, propose ways to advertise, and develop strategies on how to do it better,” Lobov, who’s based in London, told Insider.

While Lobov had been interested in programming since childhood, he had no academic background in computer science — he had a degree in advertising and public relations from Moscow State University.

“I wasn’t feeling fulfilled and started looking for something that would pique my interest,” he said.

Lobov took part in machine-learning competitions in his spare time

Lobov said he discovered “Predictive Analytics,” the 2016 book on data analytics by Eric Siegel, a computer-science professor at Columbia University, and was “hooked forever.”

“It resonated with my interest in programming,” Lobov said. “I was intrigued by how a machine could learn to make sense of data and help people make better decisions or even find solutions that humans would never be able to.”

While some machine-learning roles might require the kind of academic training only a Ph.D. can offer, Matthew Forshaw, a senior advisor for skills at the Alan Turing Institute, previously told Insider that “the vast majority” of those jobs don’t require quite so much know-how.

While keeping up his full-time marketing gig, Lobov started taking vacations to participate in weeklong hackathons and regularly competed in online competitions by Kaggle, a data-science community tool owned by Google.

“At the beginning, I didn’t understand what questions to ask or where to find guidance,” he said. But he added, “After years in the field, I think I’ve covered most of the gaps in my education to a level when I think it’s hard to tell I don’t have a STEM background.”

Don’t aim to be a grand master, but expect to work hard

Lobov said that by the time he felt confident enough to start applying for jobs in machine learning, his lack of a computer-science background could sometimes make hiring managers wary.

“An interviewer would drill you more in the technical and mathematical details than if you had another background,” he said, recalling one supposedly “nontechnical” interview in which the recruiter called on him to write a series of definitions from AI theory “just to see if I could do it.”

Lobov managed to combine his two passions in 2016 when he was hired as a machine-learning engineer by Criteo, an adtech firm. About three years later he landed a job at DeepMind.

For those hoping to emulate his success, Lobov has a simple message: “Don’t get discouraged by fancy words and math-y papers. Most of the ideas are simple; you just have to learn the language.”

Aside from “Predictive Analytics,” Lobov’s other recommendations for the uninitiated include “Introduction to Linear Algebra” by Gilbert Strang, “Understanding Analysis” by Stephen Abbott, and “Machine Learning: A Probabilistic Perspective” by Kevin P. Murphy.

“Get your linear algebra, basics of analysis and statistics,” he said. You don’t need to get it all at once — start doing a machine-learning course and then go back when you don’t understand something.

“But don’t aim to be a grand master,” he said.

Do you work at DeepMind or Google? Do you have a story to share? Contact reporter Martin Coulter in confidence via email at mcoulter@insider.com or via the encrypted messaging app Signal at +447801985586.



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