How to Write a Better Data Science Job Description
You’ve been looking to make a data science team, but you can’t get the right person. Too many terrible applications. Not enough talent. As data science becomes an integral part of business, business leaders are looking to add this crucial position to their teams. Unfortunately, confusion about terms and experience can cause companies to miss the mark writing a data science job description that will attract the right candidates.
I recently wrote a piece helping sort out the mess for job seekers, but that’s only half the issue. Businesses need to get familiar with what they’re actually looking for to help alleviate some of the confusion. The most significant area of confusion is that companies aren’t familiar with the different aspects of data science and don’t know how to identify the real job title. Let’s unravel what the job titles mean to give you the best start for your search.
[Related article: Here’s Why You Can’t Hire a Data Scientist]
What’s the Real Job Title?
“Data scientist” gets thrown around a lot, but you may not get exactly what you want using this term as a catch-all. Data science has a wide variety of job titles that better describe your ideal candidate. Choosing the right one could help narrow candidates down more efficiently.
So how do you sort it all out? Let’s imagine your data is a physical building. Here’s how some common data science job titles would interact with that “building,” i.e., your potential data.
- Data Scientist: Understands how to best use the building so that nothing is wasted. Sees potential value in areas of the building not being used. Understands the story of the building and can tell its story without embellishing or underselling.
- Data Engineer: Builds pathways and frameworks that allow people to do their work. Alters, fixes, problem-solves, and innovates the environment of your building.
- Data Analyst: Knows how to find aspects of the building that may be hidden. Maintains the integrity of the building. Focuses on the technical aspects of the building and acts as the gatekeeper for the building itself. Knows the ins and outs of the numbers and statistics for the building.
- Data Architect: Maintains the structure of your building. Creates a plan for managing and maintaining the building. Holds the blueprints for your system, your pipeline, your sprint, and your mission concerning the building. Understands how to protect the integrity and security of the building itself.
- Business Analyst: Understands how the building translates to direct business value. Can analyze key aspects of the building to maximize ROI and KPI and, most importantly, can translate that information for nontechnical personnel.
- Data Manager: Handles both the direction of the building and the people involved. Understands technical aspects of the building but also the social aspects of the people involved in various parts of building structure and process. The point person between the building team and nontechnical team.
- Database Administrator: Maintains the logs of the building. Ensures the integrity of information, including its storage. Has the historical knowledge of the building’s use and structure. Can be trusted to provide the best avenue for moving forward based on past details.
What About AI?
As businesses integrate machines into data processing, specialized job descriptions may be appropriate. We’re still working with our building, but now we’ve outsourced some of the building responsibilities to a third party company to help process and maintain. That third party is your machine learning system, and it aids your team while under supervision.
- Data Engineer: Makes the building available and comprehensible for the data science and machine learning team. Ensures there are no obstacles to performing building duties and inquiries.
- Machine learning engineer: Shares the environment and framework with the third party to help establish a base for learning and evolving. The better your framework, the more robust services the third party can offer.
- Machine learning scientist/researcher: Takes input from the building itself and finds new ways to use it. Provides innovative solutions based on what the third party finds and helps facilitate collaboration between the building’s occupants and the third party.
Finally, if you’ve moved beyond machine learning and want to explore the deep insights provided by your data, you may want to bring in the big guns, AI. AI is also like a third party, but unlike machine learning, works unsupervised to provide insights your team wouldn’t have the time or capability to unravel. As a result, that leads us to a final job description.
- AI Engineer: Highly specialized. Takes input from the building, the occupants, the third party, everything and transforms it into insights and innovations through unsupervised learning and inquiry. Knows how to keep things in check despite the lack of intervention. Can tweak input to help power real innovation.
[Related article: Why You Still Need Business Intuition in the Era of Big Data]
Understanding the Job Title
Once you’ve unraveled the job title, the qualifications unfold from there. The internet is full of resources outlining what each type of job title should be familiar with, but be sure you leave room for output and not just experience. Just because someone isn’t familiar with a particular language or framework doesn’t necessarily mean they aren’t a good fit for your team. Instead, look at what they’ve accomplished using the skills they have to find the spark.
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