Skills to unlock the best compensation

With the growing demand for data scientists, companies face an uphill battle to recruit enough of these skilled technologists to help analyze massive data sets for crucial insights.

A recent report from DevSkiller found data science to be the fastest growing IT skill set among its customers in 2021. Data science jobs are expected to grow by 22% by 2030 , according to the US Bureau of Labor Statistics.

With the growing demand, wages are also rising. According to Emsi Burning Glass, which collects and analyzes millions of job postings across the country, the median salary for a data scientist currently stands at $112,359, with that number only increasing with skills and experience.

Skills That Increase Data Scientist Salaries

Although the 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 that data scientists are upgrading their skills if they want to stay in demand. .

Sachin Gupta, co-founder and CEO of developer recruitment platform HackerEarth, also stressed the need to master programming languages ​​and libraries. “Proficiency in programming languages ​​such as Python, R, SQL, Java and knowledge of Python libraries such as Scikit-learn, TensorFlow and PyTorch tells the recruiter that you have the skills to do the job,” he said. he declares.

Gupta thinks 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 definitely helpful when looking for a job as a data scientist or trying to negotiate a higher salary: “Beginners can choose from online courses offered by Google, IBM, and Microsoft, and participate in boot camps.

Meanwhile, experienced data scientists can consider professional certification from Certified Analytics Professionals (CAP) and the Data Science Council of America (DASCA).

James Ma, data scientist at Glean, added that SQL is “pretty much non-negotiable at this point” for data science roles. “I see SQL being underrated in courses aimed at the data science crowd in particular, usually in favor of languages ​​like Python and R,” he said. “These are important; however, I think SQL belongs next to, if not above them, in terms of importance. After all, in almost every interview for a data science role these days, you’ll be asked to demonstrate proficiency in SQL, but not necessarily Python or R.”

Other skills can open salary discussions

Communication skills are also essential, as data scientists sit at the intersection of technical and non-technical audiences. If you can convey to a hiring manager that you are capable of talking to your team (and other stakeholders) through huge challenges and complex projects, your chances of landing the job increase; once you get the job, mastering your communication skills can be key to getting raises.

“A big part of the job is to use the information you’ve gathered to guide decision-making in the rest of the business, and the extent to which you can effectively translate statistical techniques and concepts into interpretable and persuasive arguments can be an important factor in the value you provide,” Ma said.

Dr. Nandi Leslie, Chief Data Scientist and Engineering Researcher at Raytheon Technologies, believes that the main skill sets for data scientists currently include (but are not limited to) statistics, machine learning, programming computer science (eg, python, C), graph theory, and stochastic processes. Higher education, with particular emphasis on a master’s or doctoral degree. in mathematical sciences, certainly strengthens the portfolio of data scientist.

“Seeking mentorship and advocacy from senior executives in desired positions is critical to learning what is valued and cutting edge in their industries of interest,” added Leslie. In general, it is advisable to participate in technical conferences and societies and to innovate with new and valuable contributions to the field of data science, whether through publications, intellectual property (e.g. patents) or technical reports.

In the context of business

The talent shortage and growing need for data science specialists means there can 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 to final analysis, if you want to prove yourself truly indispensable.

According to SlashData’s Q3 2021 analysis, most data scientists and machine learning specialists focus on only a few parts of the overall data science/machine learning (DS/ML) workflow. ML). The highest percentage is involved in data exploration and analysis, and far fewer are involved 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 with you,” Leslie said. “You may have to work a bit on your own on a large program portfolio.”

Gupta also pointed out that data cannot be isolated; to add value, it must make sense in the broader context of the business. “For data engineers, this means having the business acumen and foresight to predict trends and behaviors and distill raw numbers into actionable insights for product manufacturers,” he said. he declares.

This means that knowledge of artificial intelligence (AI) 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 big money ambitions should focus on developing and expanding their skills, and the money will follow.”

Sean N. Ayres