Want to be a data scientist in 2023? Here’s what you need to know

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Want to be a data scientist in 2023? If so, you are not alone. But rapidly changing economic conditions and recent massive layoffs at companies like Meta can have a lot of almost 106,000 data scientists in the United States and those looking to enter the field – a field in which the average salary is $100 274 per year – wondering what the coming year will bring. What skills will be most in demand? What does a typical day for a data scientist really look like? What are the biggest industry trends?

Daliana Liusenior data scientist at machine learning company Predibase and podcast host of The Data Scientist Fair, likes to ask and answer these same questions. In fact, she started her podcast — which now has 55 episodes featuring interviews with data scientists from companies like Meta, AirBnB, Nvidia, and Google — because she felt data science needed more dialogue about trends, skills and lessons learned, directly from the voices. real professionals working in the sector.

After working as a senior data scientist and senior machine learning instructor for Amazon Web Services (AWS), Liu said she knows what it’s really like as a professional in the field.

“I can share tips that I didn’t know when I started,” she says, adding that she sometimes felt alone on her career path. Data science, she explained, can sometimes feel siloed, especially with remote work.

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“I felt there was a gap between what I learned in school and what I actually do, and I also feel very anxious at times,” she said. “I didn’t know many other data scientists who worked in the industry, so I wish I could have had a community and talked to them.”

No one molds for a role in data science

Essentially, Liu said, a data scientist takes something raw and translates it into something meaningful. The power of data science, she explained, is to make sense of the past to make a recommendation for the future.

“A data scientist is basically someone who solves a business problem with data,” she explained. “I created a meme with Sherlock Holmes looking at different pieces of evidence, except we have hundreds, thousands, millions more [pieces of] proof that Sherlock Holmes – and you need to find a statistical framework or a machine learning solution to answer a question.

Data Scientist: Basically, Sherlock Holmes, only with 10,000 times more information to study.
Meme created by Daliana Liu.

What sometimes complicates the outside view of data science are the many paths professionals take to enter it and the niche skills they develop along the way. For example, Anaconda State of Data Science 2022 report found that 20% of students hoping to enter the data science profession say that one of the biggest barriers to entry is a lack of clarity about what experience is actually required. And, those already working in the field report that their responsibilities are pervasive – system administration, real data science or engineering, cloud engineering, research or even education.

Liu says that was her experience too, and many of the data scientists she’s interviewed and worked with have said the same thing: there’s just no mold to fit a role. data science – and you don’t necessarily need to have a technology background.

“A lot of people I’ve interviewed come from a non-technical background,” she said. “They’re just very interested in getting insights from the data.”

And there are different types of data scientists, Liu pointed out. There are the generalists, who have a fundamental toolkit around statistics, machine learning models and forecasting. And there are data scientists who are more specialized, working with product teams and helping the company conduct experiments or make decisions.

3 major misconceptions about data scientists

Throughout his own career and podcast interviews, Liu has observed three major misconceptions about the profession:

1. Everyone thinks you’re a math whiz.

“People think you have to know a lot of math or have a doctorate,” Liu said. But actually, she explained, thanks to tools like Python or different data science packages, you don’t have to calculate everything. That said, “you have to understand the foundation, and I think anyone can learn that.”

Liu added that she doesn’t think she’s a math “genius”. In fact, “I struggled a lot in my undergraduate degree,” she said. Overall, she added, no one is “cut out” to be a data scientist. “I don’t think I was ‘cut out’ to be a data scientist, I failed,” she said. “Everyone has struggled and they are still trying to figure things out. We all always try to go to Google or StackOverflow to find answers.

2. Data science is like magic.

“People say what we do is some kind of magic, but in reality what we often do is just hang out with the data,” Liu explained. “Some people call it ‘being one with data’ – you want to start simple and use data to understand how your solutions work.”

And, she added, keeping it simple and simple is sometimes the best way to do data science. “The simple solution sometimes works best,” she said. “I’d rather hire someone with good basic skills, then have someone who still talks about those advanced skills but doesn’t really know what they’re talking about.”

3. Solving intense technical problems is the only way to communicate.

Data science is not just about technical skills, Liu reiterated. Often these are soft skills such as empathy and understanding.

“In addition to looking at and really understanding the data and creating models, we also talk to the company’s product managers,” Liu said. “You have to have empathy for your stakeholders because ultimately your data science or insights change people’s behavior or change business aspects. You have to educate people and explain things.

What will data science jobs look like in 2023?

With uncertainties about a looming recession and more layoffs, many questions arise about the future of the data science profession. But Liu says there are key technical skills and personal traits that will hold firm even in turbulent times.

These include providing return on investment to solve business problems; the ability to clearly interpret models and their results for stakeholders; and prioritizing empathy for end users while resolving issues.

“You have to think like a business owner, even for machine learning,” Liu said. “You [might] have a lot of very technical skills [and] understand patterns, but you also just have to think because you want to solve a business problem. »

She also expects gender and racial diversity to continue to increase in the field, and says she’s already noticed that happening.

While the statistics may be daunting — Anaconda’s report notes that in 2022, the data science profession is still 76% male, 23% female, and 2% non-binary — Liu knows that will change.

“Do not wait [to see more] people who are like you to do what needs to be done,” she said. “Maybe you don’t see a lot of people who look like you, but maybe it’s more of a motivation for you to become one and then be the representation, so other people can see you. and feel inspired.”

Liu’s biggest piece of advice really has nothing to do with data science: “Strike a balance between finding value for the business and having a fulfilling, balanced life for yourself.”

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