The world of technology revolves around data. Every organization is collecting vast amounts of it from multiple sources, spending big bucks on storing it, and then processing it to make their organization better, faster, and stronger. Irrespective of the sector or field you are in, data analysis helps with better decision-making, improving business approaches, preventing problems from escalating, problem-solving, ensuring goals are achieved faster, and enabling better business strategies.
Data is the single biggest advantage an organization can have. But there’s a problem; a shortage of data professionals to manage it correctly and massage out the best insights and predictions.
Data professionals have a wide range of expertise and specializations. These can be in areas such as data engineering (transforming data into useful information with analysis), data mining and statistical analysis, cloud and distributed computing, database management, and architecture, business intelligence or machine learning, and data visualization.
The demand for data professionals has been high over the past few years. According to data on LinkedIn, data science jobs have seen a 650% increase since 2012. Statistics by Glassdoor support this figure, with about 1,700 data science job postings the site hosted in 2016. The number of these jobs increased to 4,500 in 2018 and plateaued at 6,500 in 2020 (the plateau is attributed to Covid). A report by the US Bureau of Labor Statistics predicts an increase in data science jobs skills and a growth of 28% in the sector in the coming years, right through to 2026. As demand for data professionals increases, so does the demand for upskilling of line of business users to keep up with changing operation styles.
It is predicted that a record number of data workers will begin resigning from their jobs as the Covid pandemic loses its novelty and “life as usual” returns. So far, reports show that the highest rate of resignation is among mid-level employees and in the areas of healthcare and technology. This “great resignation” is the best opportunity for an organization to grab exceptional data workers.
Recruiters and organizations will have to change their hiring processes to align with the changing market environment. There are several problems recruiters are currently facing when hiring data workers.
Challenges when hiring data workers
HR teams and data professionals have identified significant barriers to successful hires. Organizations must adapt their processes or risk missing out on great employees.
Long hiring processes
The hiring process for data workers is long and difficult, a considerable barrier for employers and potential employees. One of the main reasons for this is the long qualification cycle of the average hiring process. With data science being ever-evolving, companies are often unsure of their requirements. There is a disconnect between human resources and the teams that require the data professionals.
This leads to arduous interview processes, often over five to six interactions, with practical tests and candidates making presentations. During this time, if the required job profile changes, a whole new set of candidates will have to be considered or the same candidate may be asked to interview for a different set of criteria. The lack of clarity between the needs of a company and how a data scientist can address this is where the problem lies.
Going through many rounds only to not get the role is incredibly disheartening for potential employees. If the process is too arduous, requires a lot of time, or if many candidates are being interviewed, great candidates may simply drop out.
Mismatch in requirements and qualifications
Often, the lack of clarity on the actual need for a data scientist is the reason these professionals are mismatched with roles. If a company requires a data scientist with advanced expertise in artificial intelligence (AI), business intelligence (BI), and machine learning (ML), the average scientist is under-qualified. Vice versa, when a highly qualified data scientist, working with ML and AI is hired to create pretty graphs for presentations, there is a mismatch in qualification and requirement. This leads to a demotivated data scientist team and no benefit for the company.
Prioritizing technical prowess over potential
An increasing number of data science professionals interviewing for various positions find that their technical prowess is prioritized over their potential. The search for the perfect candidate can often lead to the loss of good candidates with potential for greatness. Hiring teams have filters that are not in line with the reality of what is required in a data science role, and ideal candidates are filtered out.
Focusing too much on qualifications on specific variants of data technology also leads to the loss of candidates.
Increased demand for remote and flexible work options
Covid has forever changed the way the workplace functions. People have found that working from home or with flexible hours does not impact their productivity negatively. This means that many people who don’t require a physical presence at work are looking for that flexibility to continue. This Covid effect is among the main reasons for The Great Resignation.
Companies looking to fill full-time, office-based positions find it difficult to get the right candidates to interview. The fear that a company may underperform with staff working on flexible hours or remotely is largely a myth, and organizations must become flexible or risk losing great candidates.
Tips for recruiters hiring data professionals
With the disconnect between reality and perceived needs occurring at the recruiting level, there are some best practices organizations should adopt when hiring data professionals.
Identify the problems you’re hiring for
Often, companies are unsure of the way data scientists can help them. The result is a hire that may be over-qualified (or underqualified in some cases), and a professional mismatched to the task at hand. It’s important to first understand what data problems you are aiming to solve.
Ensure that you have a clear requirement on the qualifications needed and proceed to filter candidates on this basis. Ask other data science employees for guidelines, rather than previous templates or non-tech staff hire protocols. This then gives you the right pool of professionals to make a choice from.
Consider your organization’s growth map
Recruiters can tend to hire for a specific problem that a company is facing. In the case of data science, it is important to hire with a forward vision. These are professionals who can be upskilled and who can take the company of the growth trajectory that’s charted out. Looking for leadership and visionary skills is equally important. Communicating this future vision to a prospective candidate be a motivating reason for them to come on board.
Close potential candidates quickly
Currently, interview loops for data science professionals can go on endlessly. This is demotivating for a candidate. Once you have a clear vision of the kind of employee you are looking for, hiring quickly is key to getting the right talent on board. It’s likely they’ve applied for other roles too, make sure you get in first.
Hire with an eye on potential
Broaden the scope of hiring a data scientist. Besides the right skills, look at their potential to work with theory and apply it to organizational problem-solving. Ask them if they’re interested in upskilling and what they think they can bring to the table to take the company forward with data. A good data scientist goes beyond their ability to work with data and must also be a good communicator, researcher, presenter, and proactive worker.
Data science employees are valuable
The importance of a data scientist cannot be underestimated. There are several advantages they bring to an organization. They:
- Mitigate risk using data to identify potential risks
- Help in the creation of more relevant products and services by analyzing data from across the company
- Enhance customer experiences and reduce churn, which is a defining factor in the success of a business
- Enable better decision-making with quantifiable, data-based evidence to back their solutions
- Open opportunities for a company and help in refining the definition of an organization’s target audience
Hiring the right data scientists can enhance the way a business functions. Data analytics can help with the HR process to ensure a good fit and a productive, long-term employee. There are many analytics tools available with Alteryx that can be applied to hiring.
An example is if Company X was looking to hire a data scientist with the specific skillset of data mining. The role that needed to be filled was within an existing team. The company was inundated with 400 resumes. Using Alteryx Intelligence Suite can help by narrowing down the top ten candidates who matched the list of requirements. It reduces bias by removing the names of the candidates during its analysis, so the recruiters hire on merit alone. The Intelligence Suite does this quickly, allowing for in-depth interviews with promising candidates.
Data science brings together multiple disciplines of programming, statistics, and mathematics. Good use of data science helps a company improve the functioning, services, and customer satisfaction it provides. Hiring the right data professionals to tap into its potential is key to the success of a company.
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