Data science can be applied to the hiring process to help human resources work smarter — and be more successful in finding those elusive candidates.

Today’s unemployment rate in the U.S. stands at 3.9 per cent. Talent managers, recruiters, and human resource professionals understand that the realities of the current labour market require using every available tool to fill the seats on the bus. One tool these professional career placement experts didn’t have in the past is data science. Data science can be applied to the hiring process to help human resources work smarter — and be more successful in finding those elusive candidates. Here is how HR and data science can team up to grow your business.

 Solving the employment problem with data science

Finding the right fit for the job when the best candidates are probably already employed is the current challenge facing talent teams. In this difficult environment, recruiters and talent acquisition managers must find new ways to gain a competitive edge. Data science can help.

According to SHRM, 47 per cent of HR leaders cite employee retention and turnover as their top workforce management challenge, followed by recruitment and corporate culture management. Furthermore, Allegis Group found that 83 per cent of employers believe attracting and retaining talent is a growing challenge. Data science can help relieve these problems for HR departments.

Data science is an exercise in analytics made actionable; it’s a way to look at the “what” and “whys” of the current processes to determine what activities are working and what should be discarded. It can replace manual processes with computer-aided efficiencies, allowing human talent teams to focus on one-on-one candidate screens.

To do this, of course, HR managers must have a way of consistently tracking the activities of their recruiting team. Most enterprise organisations have applicant tracking systems, but even the data from a simple Excel spreadsheet can yield actionable insight.

Once this data is captured it can be analysed in the following ways:

• To look for candidate profiles based on prior hiring trends. Start by reviewing data on current employees that have been successfully hired for similar roles. Once this data is collated, the hiring manager can create a portrait of the “perfect candidate” based on real metrics of past successful hires.
• To understand how much the hiring process is really costing the company. Data science can be used to create budgets and cost estimates tied to talent acquisition. Cost per hire, cost by department, onboarding costs, and certainly, the cost of failing to retain current employees can all be calculated by using data. Data science can help predict how many resumes it will take for a particular role and the candidate-to-employee conversion ratio. HR can be a numbers game; data science can help managers make sense of the metrics.
• To craft better job descriptions and advertisements for job boards and match them to the venue where they had the best response in prior years. Data science can help cull resumes by looking at keywords, then compare applicants to that “perfect candidate” persona the team developed.

Another area where data science can help is through employee retention. Using predictive models, data science can predict when an employee might potentially leave so that management can take steps to keep that employee happy. According to Kronos, 46 per cent of HR leaders say employee burnout is responsible for up to half of their annual workforce turnover. Data can track employee’s work hours so they understand who is working too many late nights and are at risk for burning out. Management can then give that person relief in their workload or days off to make sure they stay happy.

Using data modelling and predictive analysis, data science can be used to accurately project how long it will take to fill a particular role. This forecasting can affect every department, from sales to finance. It can help companies better plan for their future talent needs while helping forecast costs associated with filling these roles.

Why isn’t everyone using data

Here’s the rub; Harvard Business Review says the majority of companies are failing to harness the power of data science to improve hiring. But using these tools requires companies to reimagine a human process that has evolved over the years. Change can be difficult. Also, there is a reluctance to let software take over a very human-driven process.

However, we know that data science helps HR teams find the best match in the least amount of time possible. While no one is saying that data analytics will replace human gut instinct in the hiring process, data science is one tool to help talent acquisition managers actually find talent — a needle-in-the-haystack process in today’s job market.

For example, it’s always been difficult and time-consuming to wade through all the applications received from candidates. Data science tools can help teams cut out some of this busy work by running analytics and culling resumes that don’t match pre-defined job specifications. With data analytics and a little machine learning, the hiring process can be faster and more accurate. It is in this way that data science can help human resource hiring teams find the right people and match them to the jobs that fit their skills more quickly than manual processes.

Data science can help managers glean critical insight into what techniques have worked in the past, and which didn’t. It’s the closest thing to a crystal ball that recruiting departments have today and these tools have been proven to help streamline workflows and improve processes.

The Future Workplace found that 97 per cent of HR leaders are planning to increase their investment in recruiting technology by the year 2020, including nearly a quarter (22 per cent) who anticipate a 30-50 per cent increase in spending. Many recruiting platforms use data to find and fit the best candidate, so we can expect this technology to grow dramatically in the coming years.

Let’s face it; talent has never been so critical to the success of a business, but there’s never been such a shortage of talent to go around. That’s why talent managers must collaborate with intelligent data science tools to help them meet the challenges of today’s shrinking labour pool. Without data science, human resource managers will run the risk of falling behind.

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In this special guest feature, Matthew Mahowald, Lead Data Scientist and Software Engineer for Open Data Group, shares his perspectives on how the speed at which tech and tools have been developed, has caused problems with the way analytic deployment is made possible. Matthew holds a Ph.D. in Mathematics from Northwestern University, with a focus on the geometry of string theory and topological field theory. At Open Data Group, Matthew focuses on developing and deploying machine learning models with FastScore.

The history of predictive analytics might be said to begin with Bayes’ famous theorem relating the conditional probabilities of two events.  Even today, the importance of foundational work like Bayes’ theorem cannot be overstated: it is both the basis for most significance tests across the experimental sciences, as well as a useful tool in its own right for assessing correlation.

P(A|B)=P(B|A)P(A)/P(B)

In recent years, as the sultans of Silicon Valley have pressed both computation speeds and data storage capacities to dizzying heights, researchers and analysts working at the intersection of statistics and computer science have leveraged new tools to chase increasingly sophisticated modeling techniques. This dramatic expansion in both software tools and, especially, the quantity and quality of data available led to the emergence of data science as a discipline, and most important the assets created by a data science teams: predictive analytic models.

However historically, when it was time to deploy a new predictive analytic model into production, the burden of deployment on IT and the production pipeline was fairly minor. Long lead times meant that each model could be manually restructured (and sometimes even translated into another programming language). Moreover, the comparative simplicity of the models themselves meant that this recoding was not unreasonably labor-intensive.

The proliferation of tools and techniques in data science have not changed the fundamental deployment problem. However, the complexity of the models strains the feasibly of traditional deployment methodology. There are now more than 10,000 open-source packages on CRAN (the global R package repository). With open-source projects like Scikit-Learn and Pandas, Python offers similarly comprehensive support. Today’s vast data science environment has the ability to construct a wider variety of models faster, at lower cost, and leveraging more data than ever before.

The trend has seeped into the speed at which analytic models are being built.  What used to be a leisurely build, with a small number of fairly simplistic rules-based or linear-regression models each year, has turned into the creation of dozens of complex models leveraging the latest and greatest gradient boosting machine or convolutional neural net toolsets. As a result, the traditional model deployment process becomes simply unsustainable.

So, what’s the solution? It’s imperative that everyone – from IT professionals to data scientists – understand and address the challenges of analytic deployment in the modern era. One way to ensure that an enterprise is making analytic deployment a core competency is with an analytic deployment engine. To find success, such an engine would have properties like:

• Ensuring it’s a software component that sits in the production data pipeline, where it receives and executes models.
• It provides native support (without recoding) for any modeling language or package, that is, the engine is language agnostic.
• It can connect to any data source or sink used in the production data pipeline.

This engine should be simultaneously easy enough to use that the data science team can validate and deploy models without requiring IT involvement, and sufficiently robust and scalable that it can be used with confidence in the production pipeline.

Finally (and most importantly), an analytic deployment engine should be future-proof: new libraries and packages in R and Python shouldn’t require upgrading the engine, nor should the emergence of other new techniques and tools.

As organizations continue to gather massive data sets and develop more advanced analytic models to extract value, the number of barriers that are being encountered continue to pile up. By having the right set of data science tools that focus on analytic deployment technology, the IT and Analytics teams can find that sweet spot of success to drive ROI for their businesses.

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