Job descriptions provide insight into a place of employment by how they explain an opportunity. Their purpose is to inform potential candidates of task requirements, compensation, and various required information associated with an opening. Potential candidates should come away with a strong enough understanding about a job to evaluate if they should apply after reading. I have not always found this ideal about job descriptions to work for jobs in the data field.
When it comes to data jobs, it is often what an employer’s word choice implies, or even leaves out, that provides candidates that strong understanding. To illustrate my point, below are real texts from recent data visualization job descriptions along with what I think these descriptions can tell applicants in our field. This is a version of a career literacy game I shared with Professor Lisa Maione’s Data Visualization students at the Kansas City Institute of Art.
“You are comfortable with vague requests and able to ask the right questions to gain clarity.”
Stating vague requests as part of the job indicates an organization’s demand for data products may have outpaced their investments in data literacy. Users value reporting, but cannot yet speak on the subject. Best fit if you are interested in developing others.
“Advocates for the customer through human-centered design methods, including discovery, prototyping, research sessions, and user testing.”
This text describes a truncated version of the product development cycle. This demonstrates they are aware of that cycle and may apply it widely. Best fit for those of us looking for a more established culture of creation.
“Proven ability to effectively develop actionable business insights and recommended courses of action based on insights and influencing decision makers to take actions.”
Too many corporate buzzwords can indicate a lack of mastery, but also inefficiency. Staff may be too extended for review or this was a compromise between many approvers. This will impact your design cycle. Best fit if you prefer to gain experience quietly.
“Conducting data exploration on datasets, QA/QC, visualization, statistical and data analyses, and mathematical modeling. Creating custom visualizations that help decision makers quickly understand the data.”
The full description accurately asks for a PhD level data scientist who is also a gifted information designer. They likely developed high-end data products, employed them, but do not fully understand how to broaden usage or understanding. Best fit for pitching consultative work, or maybe Andy Kriebal.Â
Organizations in our field are less than effective when directly describing their opportunities. There are a multitude of reasons why. Mainly, job descriptions are hard to write! There are actual laws about it that change by state and that carry penalties if done wrong. Drafting descriptions often involve parties who do not have content mastery and few organizations feel comfortable being fully honest about their state of affairs. These reasons are not data field specific.
That said, a lack of clarity in task and title language from our field as well as gaps in data literacy among organizations are always going to contribute to descriptions like these. Our field is comparatively new, growing, and contains fairly abstract work tasks. Perhaps it is up to our community to provide educational documentation to organizations looking to hire more experts in data. At a minimum, we can dedicate ourselves to mentoring others in reading between the lines.
Christopher Laubenthal focuses on better data use with visualizations in an organizational setting. He has experience in both for-profit and not-for-profit sectors where he increases literacy, grows culture, and builds data visualizations. Christopher is the Data Design Manager at The DeBruce Foundation, a national foundation whose mission is to expand pathways to economic growth and opportunity. Current projects include his public viz and The DeBruce Foundation’s Career Explorer Tools.
