With the demand for data scientists growing fast, companies face an uphill battle to recruit enough of these skilled technologists to help analyze massive datasets for crucial insights.
A recent report from DevSkiller revealed data science was the fastest-growing IT skill set among its customers in 2021. Jobs within the data-science field are projected to grow 22 percent by 2030, according to the U.S. Bureau of Labor Statistics.
With demand rising, salaries are also on the way up. According to Emsi Burning Glass, which collects and analyzes millions of job postings from across the country, the median salary for a data scientist currently stands at $112,359, with that number only rising with skills and experience.
Skills That Boost Data Scientist Salaries
Although demand for data scientists is expected to remain strong as organizations focus on data-driven operational strategies, rapidly developing technologies such as natural language processing (NLP) and deep learning require data scientists to evolve their skill-sets if they want to remain in demand.
Sachin Gupta, co-founder and CEO at developer hiring platform HackerEarth, also pointed out the need for mastery of programming languages and libraries. “Fluency in programming languages like Python, R, SQL, Java and knowledge of Python libraries like Scikit-learn, TensorFlow, and PyTorch convey to the recruiter that you have the competencies to get the job done,” he said.
Gupta believes other skills that add value include cloud computing skills like Amazon Web Services (AWS) and GCP, data visualization and predictive modeling, and machine learning. A certificate in data engineering and cloud services like AWS from a reputable organization is certainly helpful when hunting for a data-scientist job or trying to negotiate for a higher salary: “Beginners can choose from online courses provided by the likes of Google, IBM, and Microsoft, and participate in boot camps.”
Meanwhile, experienced data scientists can consider a professional certificate from the Certified Analytics Professionals (CAP) and Data Science Council of America (DASCA).
James Ma a data scientist with Glean, added SQL is “pretty much a non-negotiable at this point” for data science roles. “I see SQL underemphasized in courses targeted toward the data science crowd especially, usually in favor of languages like Python and R,” he said. “Those are important; however, I think SQL belongs alongside, if not higher than them, in terms of importance. After all, in almost every interview for a data science role these days, you'll be asked to show mastery of SQL, but not necessarily Python or R.”
Other Skills Can Open Up Salary Discussions
Communication skills are also critical, as data scientists sit at the intersection of technical and non-technical audiences. If you can convey to a hiring manager that you’re capable of talking your team (and other stakeholders) through huge challenges and complicated projects, your chances of landing the job increase; once you have the job, mastering your communication skills can prove vital in securing raises.
“A big part of the job is using the insights you've gathered to drive decision-making in the rest of the company, and the degree to which you can effectively translate statistical techniques and concepts into interpretable and convincing arguments can be a big factor in the value you bring,” Ma said.
Dr. Nandi Leslie, chief data scientist and engineering fellow at Raytheon Technologies, thinks the top skill sets for data scientists currently include (but aren’t limited to) statistics, machine learning, computer programming (e.g., python, C), graph theory, and stochastic processes. Higher education, with specific emphasis on a Master’s or Ph.D. in the mathematical sciences, certainly bolsters the data scientist portfolio.
“It is critical to seek mentorship and advocacy from senior staff in desired positions to learn what’s valued and state-of-the-art in their industries of interest,” Leslie added. Generally speaking, it’s advisable to participate in technical conferences and societies, and to innovate with novel and valued contributions to the data-science field—whether via publications, intellectual property (e.g., patents), or technical reports.
In the Context of Business
The shortage of talent and the increasing need for data science specialists means there may be significant rewards for honing your skills. But keep in mind that you may need to master many parts of the data-science workflow, from data collection through final analysis, if you want to prove truly indispensable.
According to SlashData’s Q3 2021 analysis, most data scientists and machine-learning specialists focus on just a few parts of the overall data science/machine learning (DS/ML) workflow. The highest percentage is involved in data exploration and analysis, and far fewer participate in model deployment, project management, and model health and lifecycle management. Mastering more parts of the workflow can unlock more opportunities—and higher salaries.
“There may not be as much support from a large team of other data scientists working together with you,” Leslie said. “You may have to work alone quite a bit on a vast portfolio of programs.”
Gupta also pointed out data cannot stand alone; to add value, it needs to make sense in the bigger context of the business. “For data engineers, that means it is important to have the business acumen and foresight to predict trends and behavior and distill raw numbers into actionable insights for the product makers,” he said.
That means knowledge of artificial intelligence (A.I.) and machine learning, data visualization tools, deep learning models, and natural language processing can be helpful. “Earning a higher salary is a function of the value candidates bring on board,” Gupta said. “Candidates with ambitions to be high earners should focus on building and expanding their skill sets, and money shall follow.”
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