Data scientists are crucial for interpreting data and solving complicated issues in business. Here’s how they can use their skills most effectively.
Named the most promising job in America for 2019, data scientists are now considered critical for businesses generating large amounts of information. More and more organizations are implementing Internet of Things (IoT) technology in their digital transformation initiatives, bringing in more data, and resulting in a greater demand for these professionals, who can turn that information into actionable plans.
Job openings for data scientists grew by 56% in the last year, according to a recent LinkedIn report. This increased demand is why the data scientist title has topped Glassdoor’s list of Best Jobs in America for the past three years, which data scientists confirming high salaries and job satisfaction in their roles.
SEE: How to build a successful data scientist career (free PDF) (TechRepublic)
“Data scientists help companies interpret and manage data and solve complex problems using expertise in a variety of data niches,” said Neely Dolan, senior recruiting manager at tech recruiting firm Mondo. “In layman’s terms, data scientists know how to extract meaning from and interpret data, using both tools and methods from statistics and machine learning.”
The top 10 skills expected of data scientists include coding, algorithms, big data analysis, data manipulation, statistics, machine learning, natural language processing, exploratory data analysis, formalizing problems, and communication, reported TechRepublic’s Alison DeNisco Rayome.
However, simple awareness of these skills doesn’t guarantee success. A good data scientist approaches their trade in specific ways. Here are three things data scientists need to be successful in the field.
The role of a data scientist is deeply technical, focused on statistical analysis, modeling, and machine learning, said Julia Silge, data scientist at Stack Overflow.
However, “at the same time, data scientists spend energy and effort communicating about what their work means with stakeholders,” Silge said. “The fact that I analyze complex datasets and train statistical models is great and necessary, but if I can’t explain to stakeholders what these mean, then we can’t use it to make business decisions.”
A mistake data scientists often make is taking on a model development without understanding the business goals of the model, said Saniye Alaybeyi, senior director analyst at Gartner.
Data scientists must communicate with executives to know what the purpose of their data work is from the start, to best garner business insights from the data, Alaybeyi added.
Going hand-in-hand with communication, data scientists must collaborate with their teammates, merging their technical skills with business initiatives.
“Data scientists’ main job is to discover insights,” said Alaybeyi. “There is a misconception today that the data scientist is the machine learning or the AI expert. That is not true. Data scientists model complex business problems and discover business insights.”
This requires collaborating with cross-functional stakeholders, Alaybeyi added. “This helps data scientists to understand the business usage of data,” Business people and domain experts need to be involved at this stage,” she said
The data scientists who are the most successful and make the biggest positive impact in organizations are those who are able to connect their skills to the daily function of their organization, rather than working in a silo, said Silge.
Strong data scientists never stop learning, said Dolan. “Data scientists will always need to continue educating themselves to stay up to date on the latest trends and developments,” Dolan added. “These sorts of practices are always evolving, so staying up to date on the latest trends and findings will drive career development and professional success.”
While the deep, technical knowledge shouldn’t be the only thing data scientists are focused on, those skills are undoubtedly intrinsic to the job position, said Silge.
However, data scientists cannot let their knowledge base go to their heads: “One mistake I’ve seen data scientists make is thinking that either their high level of education or deep statistical knowledge makes them ‘special’ or better than colleagues from other departments,” Silge said. “It’s important to realize that the work of other stakeholders in an organization is necessary and not of less value than the technical work of a data scientist.”
To learn more about the role of a data scientist, check out TechRepublic’s data scientist cheat sheet.