According to a Hackerearth survey of 2,500 developer recruiters and hiring managers, the most in-demand tech skill for 2021 will be artificial intelligence/machine learning (AI/ML). This is an incredibly broad field, and not all jobs will require the same skills. As your organization competes for talent, don’t let enthusiasm cloud your judgment.
It’s important to ask the right questions to really gauge a candidate’s fit for the role. Importantly, the interview shouldn’t just dive into the nuts and bolts of AI; rather, it should give you an understanding of whether a candidate can translate their AI skills into business results.
[ Need to speak artificial intelligence? Download our Cheat sheet: AI glossary. ]
Here are a few questions that we’ve been asking at LatentView Analytics to help us get to the heart of the interview.
1. What's your experience with AIOps or MLOps?
Though not all AI roles require the same skillset, certain skills or backgrounds are almost universally valuable.
We’ve worked with numerous organizations that have invested millions of dollars into AI initiatives, and the pandemic has prompted their boards to ask how these have led to real growth. The focus is now on holding AI accountable and how to truly operationalize AI. It’s about better operations, not big new projects. Because of this, AIOps has jumped to the forefront of desirable skills within AI.
[ What skills are hottest right now? Read also: IT careers: 10 critical skills to master in 2021. ]
In the same way that DevOps revolutionized how we develop apps, AIOps is standardizing AI pipelines, generating in-depth performance analytics, and automating workflows. This leads to greater efficiency and also helps teams iteratively improve algorithm performance using real-time metrics. Application performance monitoring (APM) is a huge contributor to this, and we look for candidates who have experience working with APM platforms precisely because of how their performance-driven background orients the initiative toward measurable business objectives.
[ Read also: What is AIOps? Benefits and adoption considerations. ]
2. What are some novel use-cases for AI in X field?
This question really depends on the role for which the candidate is applying. For instance, if your enterprise is in banking/financial servces, you could ask about a loan approval.
The candidate’s answer should touch on a few use cases that show they are knowledgeable in your field. It should also demonstrate technical expertise in terms of relevant algorithms. In the case of loan approval, for instance, the KNN algorithm is a good Supervised Learning algorithm because it effectively sorts applications into two classes: approved and disapproved.
Most importantly, you want the candidate’s answer to translate from technical to practical. How can you ensure that your training data is unbiased and doesn’t lead to the wrong person being disapproved? How can advances in explainable AI (XAI) help us understand and justify automated loan decision-making in the case of challenged results? If they are thinking through these implications, you know that they understand your business and the potential challenges they will encounter on the job.
[ How does your AI strategy handle security and privacy? Read also: Artificial intelligence (AI) and privacy: 3 key security practices. ]
3. What emerging areas of AI research are you excited about and why does that matter for our business?
Because the supply of qualified AI experts is relatively constrained and the field is progressing at breakneck speed, your new AI hire may well be further along the cutting edge of AI research than many of their peers.
This question gives you a good idea of how their role might evolve in the future and how they perceive your company’s technology trajectory. It also shows that they are keeping up with their field and will likely continue to do so at your company. This is important because flexibility and a desire to upskill are two of the most important soft skills that someone in a cutting-edge field can have.
Finally, AI experts often become advocates internally for certain technologies or areas of AI research in which they have experience or a personal preference. You need to vet this in the interview because some people will look to make a tangential use case applicable to your company purely because they are passionate about it. Look for candidates who are pragmatic in their answers to ensure that future AI initiatives are focused and relevant.
2021: The year of AI accountability
Businesses need to be strategic and holistic as they pivot from AI everything to AI accountability. This shift should not happen only on a structural or conceptual level: You don’t simply want buy-in from the CIO or CTO, who then must constantly refocus their engineers. It needs to go down to the AI hires you are making today and tomorrow. In this way, you have alignment on business-focused AI from the bottom up.
[ Get the eBook: Top considerations for building a production-ready AI/ML environment. ]