Finweek English Edition - - MANAGEMENT -

Jaime Petkan­ics was a ba­sic Excel user when she started her f irst job out of coll ege. As a recr uiter for J PMor­gan Chase, data anal­y­sis wasn’t one of the re­quired skills.

How­ever, a few months in, she was asked to build an Excel model t hat would track and report the success rates of cam­pus re­cruit­ing ef­forts. “I was to­tally out of my el­e­ment,” she ad­mits. “Excel is not a core part of a re­cruiter’s job. I was fo­cused on hir­ing peo­ple – that’s what I was be­ing mea­sured on.” But Jaime had an in­ter­est in anal­y­sis and wanted to prove her­self as a new­comer.

She started by learn­ing as much as pos­si­ble on her own. She found tu­to­ri­als on Google and watched in­struc­tional videos on YouTube. But she still strug­gled. “When I got stuck, I would ask bankers.

They build models ev­ery day, so I was able to lever­age my con­nec­tions and f ind peo­ple who had the right skills,” she says.

Over the course of two weeks, Jaime devel­oped the model. “I didn’t get it per­fect the f irst time.

There were mis­takes in the for­mu­las and peo­ple found er­rors,” she says. But she con­tin­ued to ref ine it, and be­cause of her success, oth­ers asked her to take on sim­i­lar projects. “Once peo­ple knew that I could pull data to­gether quickly – and make sense of it – I started to get a lot of re­quests.”

Jaime ad­mits t his t r i al- and- er­ror ap­proach wasn’t the most ef­fec­tive way to learn Excel, but given the im­me­di­acy of the need, it was nec­es­sary. By the time she left the job al­most three years l ater, Excel and data an­a­lyt­ics were strengths that helped her land her next po­si­tion.

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