The new route to the best jobs in finance is very different to the old one

Back in the day, there was a clear route to the best jobs with the highest pay in banking. First you started an analyst program at a leading bank, then you left after two years to study an MBA, then you came back into a higher-paid associate position and your career took off as you hopped from place to place in search of ever higher pay.

These days, it's very different. MBAs are far less popular than they used to be in finance and are no longer part of the standard career path. Now that banking recruitment has fallen off a metaphorical cliff, more people in banking are sitting tight. The best young analysts in investment banking divisions can still move into private equity, but for other people there's a danger of spending too much time in a job rather than too little. Except in data science and technology. Here, job-hopping is still going strong, and there's a new educational pathway to the top.

'Daniel' is a case in point. He graduated from Cambridge in 2014 with a degree in computer science and mathematics and got a job as an equity derivatives structuring analyst at Bank of America. Less than two years later, he quit to study a Masters in Computational Statistics and Machine Learning at UCL. When that was done, he got a job at Cytora, a company that applies machine learning to commercial insurance. Two years after that, he quit again and just joined Deliveroo as a data scientist in the algorithms department.

Daniel's career so far bears a striking resemblance to the careers of young investment bankers past - but with different qualifications and a more technical orientation. There's the same pattern of joining the industry for a couple of years and then going back to education, except that in Daniel's case he studied machine learning instead of an MBA. And there's the same pattern of moving jobs, except Daniel switched between technology firms instead of banks and the buy-side.

He's not the only young finance professional to decide that going back to school to study a machine learning qualification is the way forward. A former structured credit trader at JPMorgan, has just left to study a PhD in financial technology at the University of Cambridge. He says he's part of a trend.

"Across trading, I know people who are realising the importance of higher degrees in coding-related fields," he says. "Traders are looking to coding degrees."

He himself studied economics at the London School of Economics and initially got a job in private equity, before studying a Masters in Finance at Cambridge because PE wasn't "quantitative enough." After the Masters he went to JPM; now he's back at Cambridge for the PhD. Ultimately, he says he'd like to pursue a start-up focused on alternative payment systems. 

Not everyone goes back to university to retrain, but those who don't typically have a high level of technical expertise from the outset. Chandan Nath, for example, left Goldman Sachs (where he was an FX algorithmic trader and systematic market maker) after eight years in September 2018. He joined Google, where he's now a senior software manager. Nath worked in technology for IBM's Indian research lab before moving into banking and graduate in computer science from the Indian Institute of Technology. 

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