How this engineer made a career turn from marketing to AI

  • The rise of artificial intelligence has created a huge demand for machine learning expertise.
  • Ivan Lobov, an engineer at DeepMind, worked in marketing before moving to AI.
  • Insider sat down with Lobov to find out how he made the career pivot.

As more industries find innovative ways to apply artificial intelligence to their goods and services, companies are looking to hire machine learning experts quickly.

Recruiters, consultants and engineers recently told Insider that companies face a skills shortage in machine learning as industries such as healthcare, finance and agriculture deploy artificial intelligence. For example, banks rely on AI to help detect fraud.

Machine learning, one of the most widely 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, Google’s AI research lab. In 2012, he worked in marketing at Initiative, an advertising agency that creates campaigns for brands such as Nintendo, Unilever and Lego.

DeepMind engineer Ivan Lobov started his career in marketing

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

Deep Mind


“My job was to make presentations and pitches, suggest ways to advertise and develop strategies to do better,” Lobov, who is based in London, told Insider.

Although Lobov was interested in programming from childhood, he did not have an academic background in computer science – he had a degree in advertising and public relations from Moscow State University.

“I didn’t feel 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 professor of computer science at Columbia University, and was “addicted forever.”

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

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

While continuing his full-time marketing venture, Lobov began taking vacations to participate in week-long hackathons and regularly entered online competitions for Kaggle, a data science community tool owned by Google.

“At first 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 closed most of the gaps in my education to a level where I think it’s hard to tell I don’t have a STEM background.”

Don’t aspire to become a grandmaster, expect to work hard

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

“An interviewer would drill you into the technical and mathematical details more than if you had any other background,” he said, recalling a supposedly “non-technical” interview in which the recruiter called on him to read a series of definitions from the AI. theory writing “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 company. About three years later, he got a job at DeepMind.

For those who want to match his success, Lobov has a simple message: “Don’t be discouraged by fancy words and math papers. Most 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.

“You get linear algebra, basics of analysis and statistics,” he said. You don’t have to understand everything at once – start taking a machine learning course and then go back if you don’t understand something.

“But don’t aspire to become a grandmaster,” he said.

Do you work at DeepMind or Google? Do you have a story to share? Confidently contact reporter Martin Coulter via email at [email protected] or via the Signal encrypted messaging app on +447801985586.

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