How to Nail an Interview for a Bloomberg AI Engineer Position – Business Insider

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A battle is brewing for one of Wall Street’s hottest commodities — engineers — and Bloomberg is looking to get in its bid as competition hits a fever pitch.
The data giant is looking to grow its artificial intelligence engineering team by as many as 50 engineers in London and New York City by the end of the year, Anju Kambadur, head of AI engineering at Bloomberg, told Insider. There is also a paid internship opportunity with the AI team for students. 
Kambadur’s division, which employs more than 200 research scientists and engineers, is in charge of Bloomberg’s AI solutions across products in news, research, and financial analytics. The team has nearly doubled in size each year since its inception in 2014, he said.
The hiring push comes as Bloomberg embraces AI to take on the growing competitive landscape, which includes powerhouses like JPMorgan’s AI research team and scrappy fintechs working to democratize the advanced tech. 
Specifically, Bloomberg is looking to deploy the cutting-edge tech to more of the firm’s operations, from the way it aggregates news and transcribes conversations to the advice and insights it offers its clients, Kambadur said. 
Kambadur’s team is highly centralized, which means it oversees AI initiatives from beginning to end. They conduct research, identify problems, build solutions, and handle the scale and maintenance that comes after launching new code into a production environment. 
As Kambadur builds out his team, he said he’s looking for candidates who demonstrate an understanding of machine learning fundamentals, computer science basics, and interpersonal skills. 
“You’re a person who likes solving problems, and at the same time you’re a person who wants to learn about how theory pans out in the real world,” Kambadur said. “You’re a person who loves working with people and not just other AI experts, but a broad group of editors, journalists, research analysts, and clients.” 
The application process starts with a resume review from a diverse committee of recruiters, Kambadur said. Bloomberg’s AI research engineering group receives up to a couple thousand resumes annually.
Candidates that pass the resume review can expect to get a call from a recruiter who will outline the process and position. Then candidates can expect two rounds of technical interviews over video, testing their machine learning and computer skills. 
Once a job seeker passes those, there are four more in-house interviews, three testing more technical skills.
There’s no set of standardized tests, Kambadur said — the case study questions are usually derived from the applicant’s previous experiences. They will probe an applicant’s machine learning knowledge, both theoretical and practical, and can be based on hypothetical situations. 
For instance, if the candidate worked at a company that detects spam emails, Kambadur would not only ask about how to detect spam and the theory behind it, the potential automation use cases, and the privacy implications, but also about scale: “You have a million subscribers,” he said as an example. “How would you set up your system so that a million models can co-exist at the same time?” 
During these technical rounds, interviewers will be looking out for candidates to explain the thought process behind their mathematical models.
In the scenario of the email spam filter, Kambadur said he would want to know if it’s a regression or classification problem; if it’s a classification problem, is it either a multi-class classification or binary?
In addition to analyzing technical prowess, Kambadur said he prizes interpersonal skills like collaboration and communication. The ideal candidate wouldn’t be shy to ask questions, he said, and they wouldn’t hold information to themselves. 
The final in-house round is a sit down with senior management to have a transparent discussion about the role, culture, and values. 
“You want to walk away from the interview with an impression that this is a person that can be quite independent and, at the same time, be very collaborative,” Kambadur said. “They’re not a competitive performer, they’re a collaborative performer. Communication and collaboration is a big part of what we do.”
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