As companies continue to expand their use of analytics, artificial intelligence (AI) and machine learning (ML), demand for data scientists is skyrocketing. According to the Bureau of Labor Statistics, data science jobs are expected to increase 31.4 percent between 2020 and 2030, making data science one of the country’s fastest-growing occupations. Data scientist is also third on Glassdoor’s list of Best Jobs for 2022, and a closely related title, machine learning engineer, is fourth on LinkedIn’s list of Jobs on the Rise 2022.
In fact, demand is so high, that companies can’t find enough qualified applicants to fill the available jobs.
Many full-time data science jobs require an advanced degree, several years of experience and a very specific skill set. While there are potential candidates for data science jobs, “only four technical requirements and three years of experience eliminate 98% of the candidate pool.”
Companies have responded to the shortage by increasing salaries for data science roles. But because acquiring the necessary skills and degrees is both difficult and time-consuming, it may be many years before there are enough candidates to fill the need. In the meantime, a growing number of organizations are compensating for the shortage by empowering more employees to become citizen data scientists.
A citizen data scientist is someone who creates models using advanced analytics or predictive tools but whose main role is not in statistics or analytics. These people have data science-like skills but may not be as advanced as data scientists. Citizen data scientists might work in marketing, sales operations, finance, pricing, IT, cybersecurity or any number of other fields. They typically have just enough data science skills to run the applications that they use for analytics. But because they couple those skills with advanced knowledge in their areas of expertise, they have the ability to deploy AI and ML in innovative ways that lead to greater success for their companies.
However, in order to unleash that innovation, organizations need to empower citizen data scientists with the right tools and support.
Case Study: Epsilon
One company that has successfully empowered citizen data scientists is Epsilon. Headquartered in Dallas, Texas, the company offers marketing services that underpin many of the most successful loyalty programs among Fortune 500 companies.
Epsilon manages data analytics for the Abacus data cooperative. This group of more than 3,000 brands shares buying data, allowing them to target prospective customers more accurately. In a typical year, Epsilon creates more than 100,000 ML models, and frequently processes data sets that measure in the terabytes. With an operation of this size, it made sense for the firm to automate as much of its data science as possible.
With this goal in mind, the company deployed H2o.ai Driverless AI and Dell infrastructure. This allowed Epsilon to streamline its processes while still enabling full visibility into the models so that the company can make sure it is not introducing any bias.
The solution also allowed members to target 15,000 more highly relevant prospects with their direct marketing campaigns, and it boosted response rates by 3-5%. That may seem like a small increase, but it generated huge revenue growth. In just one campaign, the H20.ai models helped to bring in an additional $9 million in revenue. And since Epsilon manages more than 8,000 campaigns per year, the overall impact for the entire Abacus group was tremendous.
The Power of Validated Designs
In order to empower citizen data scientists to achieve results like these, organizations need to provide their technologically savvy employees with the right combination of hardware and software. One of the fastest ways to do this is with an engineering-validated design.
For example, the new Dell Validated Design for AI – Automatic Machine Learning includes H20.ai Driverless AI for automatic machine learning, NVIDIA AI Enterprise Suite™ for cloud-native AI development and deployment, delivered on VMware vSphere® with Tanzu™ on an engineering-validated and optimized Dell infrastructure stack comprised of VxRail V670 or PowerEdge R750xa servers, PowerSwitch networking and PowerScale F600 storage.
Key benefits include:
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- AI simplified: Automatic machine learning makes it easier for everyone to train AI models
- Faster AI insights: Machine learning operations (MLOps) streamlines AI intro production faster
- Proven AI expertise: Confidently deploy an engineering-tested/validated AutoML solution backed by world-class services and support
Check out Dell Validated Designs for AI to automate machine learning, accelerate and improve AI results with the confidence of a proven engineering-validated and optimized solution. Visit the Dell Validated Designs for AI solutions page for more information.
