Since HR embraces the promise of data – driven decision – making, a Chief HR Data Scientist can provide leadership, guidance and governance for HR data science initiatives to scale up. This is what a Chief HR Data Scientist role looks like.
Data has quickly become the backbone of many industries, including human resources, and business functions. Data is the new oil that drives business growth and profitability in the digital – everything era. For most organizations, the shift to data – driven HR decision – making based on algorithms and machine learning is uncharted territory.
As CXOs recognize the need for HR teams to embrace data analytics, they are increasingly searching for HR leaders who can provide leadership, guidance, and technical know – how to ensure that their HR analytics investments pay off. With HR accountable for delivering business results, it is no surprise that mature companies have already started hiring Chief HR Data Scientists to contribute to the successful enabling of HR analytics.
A Chief HR Data Scientist’s job description is unique both within HR and across the data science field. An ideal candidate will require strong analytical skills, knowledge of the principles of data science and the ability to explain complex technical concepts to non-technically backgrounded HR professionals. A Chief HR Data Scientist will also advise other CXOs on business issues in addition to these skills and give them clear, actionable recommendations to address these challenges.
While Chief HR Data Scientists do not need a cut-and-dried educational background, having a firm understanding of applicable labor laws and regulations will certainly be an added bonus.
Key challenges for HR data scientists
The lack of sufficient data sets required for machine learning and AI algorithms will be one of the most important data issues for Chief HR Data Scientists. In contrast to data from other business functions such as sales and marketing, even HR data available across enterprise-grade organisations. It may not be successful to consider how these technologies rely on historical training data, modern machine learning and AI approaches to HR data. However, for these types of situations, traditional statistical and analytical approaches may be better suited.
Speaking of the importance of large data sets to build AI algorithms, Scout Exchange CEO Ken Lazarus says, “A high – level organization will need large data sets related to the goals of its AI initiative. The data is richer, the findings are richer. As part of this, to predict future success, they will need historical data.”
In addition, as organizations continue to invest in third – party AI solutions for specific HR tasks, Chief HR Data Scientists will have to work closely with CIOs / CTOs to identify and eliminate bias – causing factors. Regular auditing of AI model performance data, testing for adverse impact in model predictions, and retraining the model are just a few considerations that Chief HR Data Scientists must make.
Another key responsibility for a Chief HR Data Scientist will be to approach the development of statistical models taking into consideration ethical principles from the get – go. With tech giants like Google and Amazon starting to create mechanisms for ethical AI development, it is only a matter of time before regulators ask organizations to have a data accountability system in place, and it will be expected that Chief HR Data Scientists will help navigate the organization through data privacy and data management issues.
