Nicole Mitich is the content marketing manager @ Census. She's carried a love for reading and writing since childhood, but her particular focus is on streamlining technical communication through writing. She loves seeing (and helping) technical folks share their wisdom. San Diego, California, United States
The data industry is exploding, inundating entry-level data folks with a dizzying array of career options on the job market. 💫 The path starts to clear as you mature and gain experience in the field, but for someone just entering, it can be incredibly overwhelming.
So, how can you possibly begin to cross the seas of data career possibilities and hone in on the “right one”? ⛵🌊
Scott, for example, took a roundabout path to the data industry. He studied business with concentrations in entrepreneurship and global business management, before taking a leap to spend four years as a management consultant.
“[Consulting] created such a fantastic skill set – being quantitative, being analytical, structuring unstructured problems, creating something out of nothing,” he recalled. “It taught me work ethic. It taught me to shoot for excellence. I got responsibilities well beyond what I was qualified for. I grew and I learned and, for better or worse, it made me who I am today.”
Not sure how to start? Try a little of ✨ everything ✨
Scott was originally attracted to consulting because he wasn’t sure what career path he wanted to follow. As a management consultant, he could work for a major airline one month and for an oil company the next month, providing him with a breadth of work experience that was invaluable to his future.
Each industry, however different, offered a chance to fine-tune his methods for answering strategic business questions. The bite-size tenure of consulting gigs quickly gave him a taste of what data was like in different industries, so he could identify what he liked and what he didn’t. ✅🙅
Eventually, Scott built confidence that he could jump into a situation with little context and get up to speed in no time. He had a notion he wanted to work for a startup, so he took a sabbatical from his consulting role and started exploring the startup community. 🗺️ As luck would have it, he landed a job at Casper as the director of data, just as the Modern Data Stack was coming into its own.
“It was a really cool opportunity to come into an industry and into a role without any of that legacy tech and just be able to learn,” he recalled. “I feel very lucky to have learned the modern data stack from the start and been there from the early days.”
Career Rule #1: Look for opportunities to learn
It might be a little counter-intuitive, but when you’re evaluating prospective employers, you have to look beyond the compensation package. A thorough business analysis of a 16-person startup might fail to impress you, but the company’s performance on paper is nothing compared to what you might learn from working with those 16 people.
“Analysis will help you avoid some stinkers, but at the end of the day, you just have to ask: ‘If I spend the next 18 months here, will I learn? Will I grow? Will I enjoy myself?’ And if the answer is yes, do it,” Scott urged.
Data talent is in high demand, and it’s only going up from here. 📈 In their efforts to land the best and the brightest, companies offer hard-to-refuse salaries and signing bonuses. That can make it hard for someone starting their data career – especially if there are student loans involved – to take a high-growth-potential job over a high-paying one. But when you prioritize learning, money often follows. Especially in a hot industry like data, the more you learn, the more valuable you become. 🤑
“The factors that motivate you ebb, flow, and change over time,” Scott said. “Not everybody is career motivated. I love solving hard problems, and people literally only come to Brooklyn Data for hard problems. For me, it’s a dream job.”
Be confident. You might know more than you think.
It’s useless (and not to mention frustrating) to answer questions when there’s no one listening. If you truly want to make an impact at your company, first you have to determine if fundamental change is even possible because, if not, that’s a huge red flag.
“There are some organizations that will never change. And in those situations, you need to leave and you need to leave fast,” Scott said. “You have a career. If you feel like you’re pushing a rock up a hill – you’ve tried for months to get buy-in to make obvious changes, you’ve tried different tactics, you’ve been persistent – and it’s not working, leave.”
If change does seem possible, start by anchoring yourself as a senior person and command respect with small changes in how you speak and behave. These changes might look different from one organization to the next, so make sure you’re receptive to what your organization needs. 👂
“Very early in my career, I had a manager that said I was acting too junior,” Scott recalled. “It’s about how you conduct yourself. The best executives are in control of their persona and how they act in a room. They project the kind of persona the organization listens to and respects.”
For example, people at the start of their data career tend to present an issue by offering multiple solutions, each with pros and cons. In many organizations, leaders prefer someone who comes in with one strong argument and then provides the information to back it up. They may want to know about the options, but they want a recommendation from the expert on which one is best. 🥇
“You need to make a decision and say, ‘There were also options B, C, and D. I recommend A because of our focus on growth this quarter,’” Scott said. “That is the biggest mind shift for people in data – to go from showing five options, poking holes in all of them, and letting someone else decide, to actually making a recommendation themselves.”
For Boris, the best part of working in the data field is not just helping people – it’s empowering them to blow past the imaginary limits they put on themselves. His greatest reward comes when he witnesses someone experiencing a eureka moment of realizing they know the answer and can confidently work with the data and make a recommendation.
“It’s like give a man a fish versus teach a man to fish,” Boris explained. “When they have that ‘a-ha’ moment, it’s a very special feeling.”
Want to learn more? You can catch the full conversation between Boris and Scott below, or on your favorite streaming platforms. 🎧
Then head on over to The Operational Analytics Club to join the conversation around this and many other data best practices. ✨
Looking for the next step in your data career? We’re hiring! 👀
.png)
