Quantitative professionals – including data scientists – are inundated with inquiries from recruiters. Many of the people that we interviewed report that they are presented with at least one new job opportunity per week and some weeks they get 2 or 3. As a result, they tend to tune out the vast majority of the roles they are sent – especially if the job descriptions are not constructed well.
Here are a handful of dos and don’ts for a good job description (definition: one that does not get immediately deleted):
- The job description must tell two stories.
- The story of the company: Who are you? What do you do? Are you growing? If so, why? Are you making an impact on society? If so, how? Do you have something unique about your culture, your structure, your leadership team? Give details, but be concise. This is 1-2 paragraphs… not a novella.
- The story of the role: Where does it fit within the team? Where does that team fit within the company? What impact does the team have on the business? Who runs the team? Are they impressive or someone I would want to learn from? What kind of growth opportunities have others on the team experienced? Once again, this should be a 1-2 paragraph narrative.
- The description of the role should be in two parts.
- Start with a paragraph that explains the purpose of the role – the impact that it makes on the company, other teams, leadership, customers, etc.
- Follow that with a list of typical responsibilities. This is an opportunity to get into much more detail. The candidate should be able to look at this and answer the question, “What will I spend my days doing?”
- Don’t use acronyms and lingo that is specific only to your company or even your industry. A lot of data science roles within the biotech & healthcare field can be filled by people with the right combination of core technical and quantitative skills from other industries. If you litter up your job description with scientific or industry terminology people will self-select out of the process.
- Don’t get carried away with your requirements. Most of the data science job descriptions that I have seen from the bio/health space come with a giant wish-list of skills and experience. Again, people look at this and say, “I only know about half of those things… this is not right for me.” OR “No human being has all of these skills – these people don’t know what they’re doing.” In reality, you probably only need about half of the items on your wish list. Even if you include caveats like “preferred” or “nice-to-have” after certain requirements in the list… people will assume that other applicants with those skills will get priority. They will hit delete and wait two minutes for the next job description to come their way. If it’s not required, then don’t list it under requirements.
- Make sure to include “soft skills” as well as technical skills in the requirements section. If the role is going to be facing off with product teams or scientific teams and they need strong interpersonal and communication skills, then be sure to emphasize this. It is often just as important and should not be buried at the bottom as a 1-liner as though it is incidental. Say WHY it is so important so that people who have these skills will feel engaged.
- A new(ish) trend is to end the job description with some additional information about the company. I think this is a great idea. If you have some links to other content about the company or the team, I would strongly encourage you to include it at the bottom of the job description. A lot of companies have well-produced recruitment marketing videos, articles, employee testimonials, blog posts, etc. If you don’t have this type of material then you can include a quote from the hiring manager or some unusual benefits that the company offers. Flexible hours and work-from-home are BIG selling points these days. This would be a great spot to feature these benefits.
Considering how difficult it can be to fill quantitative roles in the bio/health space, it makes sense to spend an extra 30-60 minutes on the job description. The finished product should tell the story of the company, the role, where the candidate would fit in, and what benefits it would have for their career. Ask yourself: would I want to work for this company?