Data scientists: Top skills that would make you a star candidate

Are you a data scientist? There are flexible and more structured methods of upskilling, be it via microcredentials, pursuing a master’s degree to taking postgraduate certificates.

By U2B Staff 

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The future is bright for aspiring data scientists. Glassdoor lists data scientists in the top five of their 50 Best Jobs in America for 2020 report.

Meanwhile, the US Bureau of Labor Statistics said demand for statisticians is projected to grow 33.8% percent from 2016 to 2026.


“Statisticians will be in demand for their ability to develop and analyse big data,” it said, adding that the field of data science will be a source of particularly high demand for these workers. 

“Data science combines methods from statistics, computer science, engineering, and management. This field aims to build models, make predictions, and recommend actions based on data, rather than just explaining what the data mean. 

“As businesses increasingly look to make decisions with fully formed data analysis and evidence, statisticians who work on actionable predictions will be in high demand,” it said. 

For instance, Northeastern University notes that almost every interaction with technology includes data — be it your Amazon purchases, Facebook feed, Netflix recommendations to the facial recognition required to sign in to your phone.

“Amazon is a prime example of just how helpful data collection can be for the average shopper. Amazon’s data sets remember what you’ve purchased, what you’ve paid, and what you’ve searched. This allows Amazon to customise its subsequent homepage views to fit your needs,” it said.

So, if you search for sporting-related gear, you’re unlikely to see ads or product recommendations on baby products, but rather those related to your search.

With data scientists being a top job that could lead to a variety of career opportunities across a range of industries, what are some of the skills needed to thrive in the role?

Technical and non-technical skills to succeed as data scientists

Course provider Dataquest notes that there’s no universal definition of “data scientist” or “data analyst” that every company agrees on, adding that different positions with the same title may require different skill sets.

Despite that, there are some hard and soft skills that could help you stand out. 

Four data science leaders at four tech companies in the US tech industry spoke to Built In about what it takes to be a successful data scientist.

Lacey Plache, Age of Learning vice president of data and analytics, said communication, practicality and curiosity are some important traits.

She said, “data curiosity is an important skill that relies on instinct. It involves constantly thinking creatively about how to use data, asking questions about why and how patterns occur and trying different approaches, interpretations and viewpoints.”


Niels Joaquin, Maven Clinic senior director of data science, opined that data scientists with a broad background in applied math — such as an understanding of some subset of optimisation, stochastic processes, Bayesian methods and discrete math — pull from different frameworks in their toolbelt to solve novel problems.

Lauren Talbot, BARK vice president of data and analytics said, “Analysis and decision making almost always precede machine learning, which means the way another human receives the output of the data scientist often means the difference between moving the needle or not.” 

Sanjay Castelino, Snow Software Chief Product Officer, said the most underrated skills for data science have little to do with data or science — it has to do with understanding the scope and impact of solving a problem so they understand where to focus efforts on getting the next 1% improvement in outcomes. 

“They take disciplined experimentation and that takes time, and you have to decide where to invest time, when to stop experimenting and when the potential return is worth additional time,” he was quoted saying. 

These insights from industry practitioners offer some insight for current and aspiring data scientists in choosing what to upskill in. There are flexible and more structured methods of upskilling, be it via microcredentials, pursuing a master’s degree to taking postgraduate certificates. Whichever you choose, lifelong learning will prove to be an indispensable part of your career journey.