Recruiters will have access to the job description that is posted, but any additional context for the types of skills, experience or interests that would most interest you in a candidate is valuable to share. Think about how many different roles, especially at a hybrid medical center and academic institute, the same HR department recruits for, then think about how much domain specific knowledge would be required in how many domains for them to be able to confidently assess what a “good” candidate looks like for any given hire.
Think about the top 1-2 features that would be of most interest to learn about a candidate that would provide some insight into the domain-specific requirements. How would a non-technical recruiter evaluate, at least generally, a candidate in an informative way? Blending that insight with all the expertise of a recruiter can create a very effective team to identify good candidates for your position.
One somewhat unique challenge for evaluating candidates in data and programming oriented technical roles is the trend that candidates with data science, research software development and other data analysis skill sets are more often obtaining skills through non-degree granting educational opportunities, or during seemingly un-related job experiences (e.g. a laboratory technician who’s learned R programming on the job to analyze the data they generate). With the rise of self-taught candidates, being able to assess those candidates fairly and with the recognition that it takes quite a bit of drive and motivation that may not immediately come across on a resume, will help you avoid weeding out excellent candidates.
“Forty-three percent of candidates today are self-taught in one or more of their role’s job requirements, according to Gartner research (2020). Resumes that reflect the ability to adapt and learn new skills – even if they don’t tick every box of desired skills – may make for a more attractive quality of hire.” If “only 16% of new hires have the skills needed for both their current and future roles” in general, when hiring into a research institution this might be even less. Read the Gartner article here
Provide Guidance for Recruiters
Hiring Managers can be proactive about supporting their recruiting team for hiring technical roles. These are some specific suggestions for after a job posting has been approved that can help. Keep in mind that often data science jobs are in high demand, and the response rate of positions in that area can be quite high.
Highlight the most important aspects of the candidate’s experience, or qualifications to the person doing screening of the resumes that are submitted.
Provide a list of skills, interests or experience types that would perhaps not be listed explicitly on the job posting but candidates who have them would be likely to be successful in the position.
Candidates will be required to meet the minimum requirements, but additional insight into the less tangible aspects you value most in a candidate can help prevent screening out of good candidates and enriching your pool for the most interesting candidates. Examples include:
- We would accept someone with less years of experience if they had specific experience working with a specific data type or in a particular context (like prior experience at a research or medical institution).
- Related job certifications might be nice to have, but would value experience over a certificate if given the choice.
- One or two specific desired skills that are most valuable.
An interesting article about hiring Clinical Research Coordinators in an academic center much like Fred Hutch documents some considerations into how this process can be challenging or improved. This article provides some useful context for what is going on on the recruiter side of the experience that might influence how you approach hiring in general, and most especially for technical roles.
Recruiters will primarily focus on some of the most critical, but non-technical, aspects of whether a candidate might be a good fit for the role during the phone screen. This may include some fairly standard questions about the candidate and questions such as why they are interested in the role or what they are looking for in their next position. To make this time invested most effective, it can be helpful to have some questions tailored to the role itself.
Provide the person doing initial phone screening of candidates some questions to ask during that screen that are non-technical but get at the 1-2 most critical aspects of the position. Giving some suggestions for what you’d consider an “interesting” candidate might answer versus an “uninteresting” candidate is valuable too.
This is a good place to ask questions that get at interests beyond their experience and also workstyle or workplace culture which are less likely to be reflected on their resumes. Questions about long term career goals or reasons why they applied to the position can give hints toward whether the trajectory of the role you have in mind matches what they are looking for, which can result in a better fit.
Assessing whether to move a candidate forward based on the results of a phone screen should be done, as mentioned, as consistently as possible across all applicants. Describing for yourself what constitutes a “good” or “neutral” or “bad” phone screen tailored to the specific position and described prior to receiving any phone screen results helps to reduce bias and help you be efficient.
Updated: August 18, 2023Edit this Page via GitHub Comment by Filing an Issue Have Questions? Ask them here.