Case Studies

This post was developed with input from Bill Franks, internationally recognized thought leader, speaker, and author focused on data science & analytics.Last week, we published a piece dedicated to Navigating The Early Career Job Interview Process as an early-career candidate in the data science and analytics space. This week we will focus on what employers should keep in mind when interviewing and hiring early-career candidates, here are a few tips for you to keep in mind:

Salary Must Match Market Rate

Salary is much more consistent for early-career candidates and often holds high importance in the mind of prospective interviewees. It is vital for the employer to know what rates are currently standard across the market for entry level data scientists and analytics professionals, along with bonus eligibility or other benefits that competitors may be offering. It is important to keep in mind that for early-career candidates, it does not take much above market value to get prospective candidates’ attention. On the other hand, small increments under market value are taken very seriously amongst early-career professionals and can potentially become a distinguishing factor between your organization and a competitor that is willing to pay $5,000 more.

Resumes Won’t be Differentiating

As a new graduate, it is very difficult to curate a resume that is unique and speaks to individual accomplishments or strengths. Most early-career candidate resumes usually have similar courses and similar limited work experience in the form of data science or analytics focused internships. Every course and internship will sound impressive, but it is important to remember that most students are entering the market with a varying knowledgebase, and it is important to focus on other factors outside of their professional or academic background.

Intentional Interview Process is Vital

Early-career candidates are much harder to filter from resumes, so we always suggest that companies spend some extra time really getting to know the interviewee. It is advisable to set up an interview structure that contains 2-3 rounds and allows for multiple team members to have a touchpoint with the prospective candidate.It is also important to bear in mind that candidates at this level are usually not polished – and it is important to be more forgiving of awkward statements or rambling answers as they generally do not have extensive experience with interviewing. Their behavior could just be a clear case of nerves and it is important to set different expectations for early-career candidates in comparison to more senior-level data science or analytics professionals.

Formal Validation of Soft & Hard Skills

It is advisable for organizations to implement some sort of standardized approach for validating programming and/or analytical skills for early-career candidates. This is necessary because although programming and analytics are covered in both course work and potentially in previous internships, all candidates are at a varying level of comfort with various tools and techniques. It is always good to assess for a general baseline to ensure that the candidate has the skills that you are seeking.After seeing where they need more support and are maybe lacking, your team can support them to be successful once onboarded through training and development. We also advise organizations to focus equally on soft skills and have prospective candidates give a presentation to assess their public speaking abilities and general comfort communicating their ideas.

Set Clear Expectations

When hiring early-career candidates, it is especially important to explain what onboarding resources and processes they will have access to and what they won’t. They should be given utmost understanding of the prospective role and responsibilities, as well as any training and onboarding the team is willing to offer.If your organization has a limited onboarding plan with minimal training, finding someone that is willing to hit the ground running and doesn’t find that to be intimidating will be the best fit. Now, if you have a structured onboarding plan that is weeks or months long and will build the candidate from the ground up, then it is important to explain this to candidates in case they are looking for something with less structure and would prefer a role where they can jump right in from day one and just figure things out.

Final Thoughts

Ultimately, the goal is to find the perfect fit for your organization – and hiring early-career candidates can sometimes become a challenge that is not given the attention it deserves. Be sure you and your organization are giving early-career hiring the proper focus. By following the tips in this blog, you’ll be better prepared when it comes to making your future hiring decisions to grow your data science and analytics team.