Solving the Skills Shortage When There’s a Lack of Data Scientists

The Data Incubator is a data science education company. It offers data science training and placement services. It’s best known for an eight-week academic boot camp preparing students with master’s degrees and PhDs for big data and data science careers.

So you want to hire a data scientist? The U.S. News & World Report ranked ‘data scientist’ as the sixth-best job and the third-best tech role for 2022. With a average salary of $100,560 and unparalleled career development, you’d think there would be hundreds of qualified candidates waiting to join your organization.

Unfortunately, that’s not the case at all. 

With demand at an all-time high for tech professionals, many data scientist jobs are left unfilled. Companies try to attract top talent with ever-increasing salaries and benefits but often can’t find the candidates they need. 

So, what’s the reason for this skills shortage?

Data science programs are few and far between, and they are usually not inexpensive. Fortunately, there are data scientists available, but they can be very difficult to find and hire and an affordable rate.

In this guide from The Data Incubator, learn more about what data science entails and how to find the best-qualified data scientist for your business.

What Is Data Science?

Data science is a discipline devoted to acquiring, analyzing and presenting data. By leveraging the latest discoveries in data theory, math, computing power and visualization, data scientists can extract useful information from data sets which can then be applied across multiple industries. 

The main approach to data science is analyzing large amounts of data to gain valuable insights into new and existing products or services. Data scientists crunch numbers, analyze data visualizations, and identify patterns and trends in large and complex data sets. That helps organizations like yours make smarter decisions.  Data science is used in multiple industries to gain visibility, predict outcomes and optimize human performance. Companies like Eventbrite, PaperG, Yelp, and LinkedIn use data science to optimize business models, and U.S. healthcare systems use it to improve public health. 

As data scientists present more accurate insights into complex data sets, the accuracy of predictions becomes more vital.

The business needs for this field are extensive. Businesses will often need to deploy data scientists in order to solve problems, interpret massive amounts of data, increase efficiency, and find new product lines.

The challenge for businesses? Data scientists are in high demand, and they are hard to find. 

So, Who Are Data Scientists, and Why Are They So in Demand? 

There are many definitions for this role, but typically, a data scientist is an individual who has a deep understanding of statistics and its use in business. A data scientist also has to comprehend a statistical programming language called R or programming languages like Python and SAS. 

In recent years, the number of companies looking for qualified data scientists has exploded as decision-makers realize they can’t analyze large amounts of data with their existing software and tools. Data science differs from analytics because it provides a full range of services for businesses by leveraging data-driven insights and information to offer better products and services for customers. 

But how can a data scientist benefit your organization? Below is a list of some common business questions  these talented professionals answer:

• What are my customers’ demographics? How do my customers compare to the general population?

• How likely is it that a customer will buy a product or service based on various past indicators?

• What is the probability of an outcome based on a set of covariates? For example, will someone default on a loan? How can we recommend products and services that customers will use?  Which customers are most likely to churn? Which people have the most influence on a customer?

• How can we improve the information quality of our data systems?

• How can we reduce costs by using data? For example, can we estimate repairs by calculating their cost? What are the most effective tools for collecting, storing, processing, and visualizing data?

• How can we collect more and better-quality data? For example, can we coordinate a greater collection of data from various sources?

• What is the value of data, and how do we best use it? For example, how can we optimize critical decisions based on past results? How should we combine various analysis types? For example, can we combine marketing and analytics to develop compelling products? How can we make more accurate predictions? For example, can we predict losses before they occur? 

What Type of Careers as Available In Data? 

There are several different options available for people with diverse backgrounds and skill sets that want to get into data. These include:

Data analyst (common in business settings) 

A data analyst is typically deployed to solve specific problems. As you would imagine, this is done by finding and interpreting data. Companies will often hire a data analyst to examine particular issues, like increasing efficiency or closing gaps within their supply chain. Data analysts will often be hired on a project basis, but many large companies will have a constant need for their services.

It is essential to realize that a data analyst can’t just be good with numbers. They also have to be good at explaining and communicating their findings to individuals who may not have the intricate grasp of data and details that they possess. 

Data engineer 

A data engineer is an IT-oriented profession. The job of a data engineer is usually directly involved in system design. A data engineer will build and optimize systems to collect data. They will also create ways for that data to be exported and interpreted by others within the data field.

Data scientist 

A data scientist often gets deployed at the beginning of a project. Their job is to figure out the specific issues, what data is needed to resolve these issues, and where that data can be found. 

Modeler

The job of a data modeler is to work with wide-ranging systems and infrastructure. They design computer systems that are supposed to model data into a format that others can easily use. 

Each path provides significantly different career experiences and pay, and this is something that businesses must keep in mind when trying to find the right data-related staffer. It is essential to differentiate between the two data science specialties (statistical analyst and data scientist). 

There are also many different job types in business intelligence, analytics, and technology that go under the name “data scientist” as well—each with varying responsibilities, education requirements and overall pay.

While the statistical analyst and data scientist roles share some similar skills, there are a few key areas where they differ. For example, a statistician will spend more of their time educating others about data and its limitations rather than communicating business problems or requirements. The data scientist role is more exploratory; the statistical analyst role is more applied.

Statistical Analyst

A statistical analyst uses statistical models to manage large amounts of data (particularly for modeling) or implements predictive models within software systems. A data scientist, on the other hand, is interested in data analysis and manipulation. In a nutshell, a statistical analyst is responsible for processing research results and generating reports in quantitative form. An analyst can expect to work with many different analytical methods and tools, such as SPSS (statistical computing), business intelligence tools, R (for stats), Apache Spark, Python and Microsoft Excel. 

“Statistical analysis” refers to the more fundamental aspects of the field—things like manipulating raw data sets or algorithms. “Predictive modeling” refers to the more complex aspects of the field—things like building machine learning models and scoring data for a business.

The top employers for statistical analysts are in the business, government, and consulting industries. The statistical analyst career path provides some great job opportunities to those who want to work in business or governmental settings but do not necessarily have the desire to be fully involved in the startup scene or independent work.

Data Scientist

Many individuals claiming to be data scientists only have a layman’s understanding of what it means or simply use it as a buzzword. To earn the title of “data scientist,” someone must be proficient in data analysis and also possess a mastery of diverse fields like statistics, programming, computer science, and business.

Some common uses for data scientists include providing input on the future of medical devices; recommending practical recommendations for online advertisements; forecasting oil prices and stock prices; predicting customer behavior based on past sales history; analyzing trends in government regulation policies; and extracting valuable data from complicated software applications such as Facebook or Twitter.

Accompanying skills that are typically necessary to be a data scientist would be programming languages such as Python, Java or R (in addition to SQL). To progress in this field, it is also advantageous to learn some machine learning and natural language processing.

A data scientist’s role is to build models for predicting or understanding existing situations. They use their technical expertise to find insights that matter. To do this effectively, they will be involved in projects from initial inception to deployment and ongoing maintenance.

Individuals in the major data science specialties are one of the highest-paid roles in data science, earning on average $98,230 per year, according to the U.S. News & World Report. One study suggests there will be a 364,000 increase in job postings for data science and analytics-related roles in 2022, proving there are many career opportunities available for data scientists. If someone wants to become a data scientist, they can get work experience as a business analyst, analyst programmer, or statistician first to make a solid case for employers. Data scientists typically have an advanced degree, and many have PhDs in statistics or computer science. Certifications for Business Intelligence Developers are valuable and can be a bargaining chip when negotiating pay and position.

Why Do Organizations Struggle to Recruit Data Scientist Talent?

It’s important to note that there isn’t just a shortage of data scientist talent but a growing gap in wider data teams. The most successful data-driven organizations should strategically grow their teams and hire top talent to fill data engineer, data analyst, machine learning engineer, and product manager roles. That can help create an excellent data ecosystem. 

Another problem organizations face is losing potential data scientist candidates to other companies or industries. Sometimes, when competition is so fierce, larger companies attract data scientist talent to their organization with extremely generous salaries and benefits packages, leaving smaller organizations out in the cold.  One way decision-makers can solve this issue is by offering potential candidates an opportunity to be a part of their organization before they even embark on a data science training program. These companies can give candidates a data science apprenticeship through their own organization or with an external program.

Companies can also train internal employees who want to start a new career as data scientists. These employees will enroll in in-depth training in all data science tools, including Python, SQL, Matlab and R. 

Using social media updates that show company updates is another method to attract talent. Companies can share case studies and business articles and connect with potential candidates. They can also create job videos that show what it’s like being a data scientist at their organization and provide a visual representation of its culture.

Finally, companies can use data science tools and platforms during their recruiting process. Many companies use these tools during the interview process, but they should also try to test these tools on “non-technical” candidates as well. For example, if someone is looking at job advertisements in a specific industry and many positions require an analytics background, companies can post their job advertisement on Reddit, Quora, Hacker News or Stack Overflow and see if there are any responses.

Finding Qualified Data Scientists When There is a Skills Shortage

It isn’t easy to imagine a future where data scientists are not doing their part to make the world better and more efficient. While it remains unclear whether the current skills shortage will improve, the best answer for organizations looking for data scientist talent is to work with a hiring partner that can connect them with qualified candidates. 

That’s where The Data Incubator comes in. By using this hiring partner, you get access to the best data scientist candidates in the world based on your bespoke hiring needs. Whether you need a scientist right now or in a few months, TDI will match you with the best candidates and streamline the hiring process. You can hire on your own schedule, meet candidates before you hire them, and reduce on-the-job training and recruitment costs. 

Want to find the world’s most talented data scientists, data engineers and data analysts? The Data Incubator trains the best professionals available today with the latest tools and technology, helping you find qualified candidates that meet your organizational goals. Hire the best by getting in touch with us today!