Tanya Cashorali speaking about the importance of data

Level recently put together a panel of data analytics experts to talk about how they use data in their careers. Timna Molberger of Visible Measures, Tanya Cashorali of TCB Analytics and (formerly) Biogen, and Dave Hoch of Localytics joined Nick Ducoff to discuss real world applications of data analytics. Data analytics is something none of these successful data analysts came into their careers knowing they wanted to do. Instead, it grew out of the fields they found fascinating.

“I took a psychology class where we were learning about the brain and neurotransmitters, and I just became fascinated with health care. And then I started to hear about this field called bioinformatics, and that sounded really interesting because I could combine my computing skills with doing something to improve health care,” said Tanya Cashorall, who was a data scientist at Biogen, a biotech firm that does drug research and development, and is now a founding partner of TCB Analytics. “The term ‘data scientist’ hadn’t even been coined yet! But it was just like solving a mystery every day, and that was really fun for me.”

Dave Hoch, a product and data analyst at Localytics, a SaaS company in downtown Boston, came to love data analytics through a series of internships he had in college. Screen Shot 2015-12-02 at 9.34.05 AM

“I just started playing around with queries and big data and that kind of stuff. There was a lot of on the job learning about how to access information and really finding out trends and insights,” he said.

All panelists agreed that data analytics is much more than an abstract concept. They use it constantly for solving real world problems affecting their companies. Tanya relates how in her previous job as director of analytics at a telecom company, the company was very invested in understanding the data around people who switched cell phone providers. The problem was that there are about 50,000 instances of customers across the United States switching cell phone providers every day. The massive amount of data took 20 hours every day to process, which was completely unsustainable. So they turned to better analytics technologies.

“We plopped it into Amazon Redshift, another columnar database,” she says. “We were able to leverage Hadoop and Elastic Mapreduce, which brought the processing time down to just 30 minutes.” 

Being successful at data analysis requires much more than just technical aptitude.“Technical skills are great, and we definitely want that, but in an interview, you need to show me a willingness that you’re going to figure things out,” says Dave. “There’s an analytical mindset of ‘I don’t know the answer, but I can figure out the answer’ that is way more important than coming in with specific SQL skills, R skills, and Python skills. You have to have a mindset of ‘How am I going to attack this problem?’”

profile-of-a-data-scientist

Timna Molberger of Visible Measures, a marketing tech firm that measures how video advertisements spread online to help brands get their content launched, agrees that the best candidates for data analytics jobs are the ones who can go beyond technical skills.

“A big thing for me is how receptive are you to feedback, because no one’s going to come in and be able to do the job on day one,” she says. “It’s that ability to take feedback, and openness to take feedback that can really make or break an employee, in my mind.”

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Timna Molberger of Visible Measures

On the perennial question of work-life balance, the panelists were optimistic about the flexibility many data analytics careers provide.

“There was an article just published about the top jobs for work life balance,” says Tanya, “And data scientist was number one for best work/life balance.” While data consulting work can sometimes have set hours and the added hassle of constant travel, data startups often tend to be flexible in their approach.

“You get your work done, and you do good work, that’s all that matters. If you want to start at 11:00 in the morning, and whenever at night, as long as you don’t have a 9:00 or 10:00 a.m. meeting you have to be at, it doesn’t matter to us. Get your job done, do a good job, and that’s really what matters for us,” says Timna.

Other Level panelists have included analytics leads from Mullen Lowe US, American Well, and Harvard Business Review. Stay tuned for more insight from data experts!