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One Of The Issues With An HR Career

Brendan Lys
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Brendan Lys
Brendan Lys

Operating at the intersection of Human Resources and Data Science, I leverage extensive specialist experience within Human Resources, with the methodologies and approaches of Data Science. This focus on the discovery of actionable insights from data, has been applied to areas such as: remuneration & benefits, workforce planning, recruitment, health & safety, diversity, and training. But what does the application of data science to HR challenges and opportunities actually look like. Within an HR framework the data we work with typically comes directly from our HRMIS, an advantage of using data science methodologies is that we can bring in additional data either held within the organization or from external sources - data which is out of reach from a pure HR analytics approach. Consider for example position descriptions, these contain a wealth of data that we typically ignore as its not in a analysis ready format. A side project I'm working on currently (April 2019) is using text mining on job descriptions to provide insights into which job family the position may fit into. The insights of my work have been enjoyed by organizations across a diversity of sectors including: Government (Australia and New Zealand), ASX and NZX listed companies, utilities, not for profit and higher education.

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In an earlier post, I shared my opposition to HR becoming a profession and the reasons for this. I stand by those comments, however, this lack of a shared understanding of what HR is and the knowledge required to practice within Human Resources does have some drawbacks. You may very well hit this drawback once in […]

One Of The Issues With An HR Career

In an earlier post, I shared my opposition to HR becoming a profession and the reasons for this. I stand by those comments, however, this lack of a shared understanding of what HR is and the knowledge required to practice within Human Resources does have some drawbacks. You may very well hit this drawback once in a while, and its simply this, people have different ideas about what HR is and the knowledge or experience you need to competently work in the area. Essentially we all have our own ideas about what an ideal candidate will look like, and these ideas will make it into the job description. Two which I encounter in my own career is a requirement for generalist HR experience or experience in employment relations. I’ve always been a specialist, so I don’t have generalist HR experience, and while employment relations is a specialist area, it's not one I’ve made the opportunity to gain experience within. Potentially with employment relations, this has been an oversight, the reason why I haven’t sought out experience in this area is that the best ER people I’ve worked with all have law degrees – and I haven’t really had the inclination to go and get one. So I’ve stayed in the specialist roles where I can leverage the degrees I do have.

So what does this all mean, it means take heart when you don’t meet every single requirement for an advertised vacancy that you would otherwise be a perfect fit for. It doesn’t mean you can’t do the job, it simply means that the person or team writing the job description have a different concept of this aspect of HR and its knowledge or experience requirements. So my advice, if you don’t tick every box on the job description still apply, demonstrate to the selection panel that you can do the job, and indeed that you might even add some diversity into the team.

Brendan Lys
By Brendan Lys

Operating at the intersection of Human Resources and Data Science, I leverage extensive specialist experience within Human Resources, with the methodologies and approaches of Data Science. This focus on the discovery of actionable insights from data, has been applied to areas such as: remuneration & benefits, workforce planning, recruitment, health & safety, diversity, and training. But what does the application of data science to HR challenges and opportunities actually look like. Within an HR framework the data we work with typically comes directly from our HRMIS, an advantage of using data science methodologies is that we can bring in additional data either held within the organization or from external sources - data which is out of reach from a pure HR analytics approach. Consider for example position descriptions, these contain a wealth of data that we typically ignore as its not in a analysis ready format. A side project I'm working on currently (April 2019) is using text mining on job descriptions to provide insights into which job family the position may fit into. The insights of my work have been enjoyed by organizations across a diversity of sectors including: Government (Australia and New Zealand), ASX and NZX listed companies, utilities, not for profit and higher education.

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