Top Interview Questions for AI and ML Candidates: What to Ask and Why?
We try to answer what you should be looking for when hiring for a sector as niche as AI and ML!
Over the past decade, there has been rapid advancement in artificial intelligence (AI) and machine learning (ML). The integration of AI and ML with a plethora of technologies has resulted in diverse use cases. Needless to say, this has also opened up plenty of job opportunities in this field, as it requires highly skilled professionals to make groundbreaking progress. However, hiring the right candidate can be challenging! You’ll need to assess their overall technical knowledge, practical expertise, and problem-solving skills.
This is where targeted questions come in. Asking targeted questions can help recruiters analyze a candidate’s grasp of a particular topic and whether they have a comprehensive understanding of how to solve challenging problems based on their knowledge.
In this blog, we took a deep dive to come up with the top questions that you can ask potential candidates before making a final call on their candidature. These questions will make it easier for interviewers and hiring managers to shortlist top-notch candidates for AI and machine learning roles.
Key Skills to Look For in AI and MLCandidates
As a recruiter or a hiring manager, when you’re planning to interview candidates for AI or ML, it’s essential to evaluate more than just their theoretical knowledge. You must look into their technical skills as well as soft skills to ensure the perfect hire who can work efficiently within a team.
Here are three essential factors that recruiters must assess in a candidate before making the final call:
- Technical skills
- Problem-solving skills
- Communication skills
Let’s take an in-depth look at each of these critical skills to ensure that you prepare the right set of questionnaires consisting of all these factors:
1. Technical skills
Technical skills are a must-have for an AI or ML role. Recruiters must pay special attention to assessing a candidate’s technical expertise. A solid technical understanding ensures that the candidate is qualified to comprehend AI and ML-based functionalities at their core.
Programming Expertise
Candidates must have a working knowledge of multiple concepts in these programming languages, including Python, R, Java, and SQL, and they should also be able to write functioning codes in these languages. Interviewers should ask questions based on this specific knowledge to analyze the candidate’s proficiency in these languages.
Here are a few programming expertise-based questions that recruiters may ask candidates to comprehend their technical knowledge:
- Python: “Can you explain how you would use Python to preprocess data for a machine learning model?”
- R: “How would you handle missing values in a dataset using R?”
- Java: “Describe a project where you used Java to implement machine learning algorithms.”
Algorithm and Data Structure Knowledge
Another crucial aspect of technical expertise required for an individual’s candidature is whether they’re proficient with algorithms and data structures, which form the backbone of these advanced fields.
It is important to have the right expertise in data structures such as arrays, linked lists, trees, graphs, hashes, and heaps. These enable candidates to write functional code, optimize working models, and solve complex computational problems to create exceptional use cases. Knowledge of certain core algorithmic concepts, such as search algorithms, sorting algorithms, and graph algorithms, is crucial for candidature.
In order to test a candidate’s expertise, interviewers can ask questions around these topics, such as:
- Algorithms: “Explain how a decision tree algorithm works and provide an example of its application.”
- Data Structures: “What data structures are commonly used for optimizing performance in machine learning models?”
2. Problem-solving skills
Candidates need to possess exceptional problem-solving capabilities in addition to a thorough grasp of technical knowledge. Working within the AI and ML space, a candidate will encounter several technical or non-technical challenges on a daily basis.
From code bugs to system malfunctions, candidates should be able to tackle complex and unstructured challenges, devise strategic solutions, and implement them to achieve the most promising results. Candidates who possess strong analytical and data-driven thinking have proved to be real assets in AI and ML roles.
Practical Scenarios
The best way to assess a candidate’s problem-solving capability is to present them with practical problems within the industry and analyze how they would solve a particular crisis. Interviewers can review existing ML case studies and develop specific decision-making questions for the candidates.
They can also test how efficiently a candidate can put their theoretical knowledge into practice by laying out AI problem scenarios. Additionally, qualities such as diplomacy, respect for ethical boundaries, and an out-of-the-box approach can also be tested to ensure a sensible hire.
Here are a few questions that interviewers may ask potential candidates applying for AI and ML roles to test their problem-solving efficiency:
- Problem-solving: “How would you approach solving an imbalance issue in a classification problem?”
- Scenario-based: “Describe a situation where you had to debug a complex machine learning model. What steps did you take?”
3. Communication skills
Excellent communication skills are crucial for working within a company. Companies require their employees to work together and drive impressive results to boost revenue. Communication skills in AI and ML communications are core strengths of a candidate.
As a team player, it’s important for candidates to have the necessary soft skills that are crucial for proper communication among each other to ensure the successful execution of the deliverables.
Explaining Complex Concepts
Candidates seeking a job within the AI and ML industry must be proficient in explaining AI concepts and their complex technical complexities. Not only does this speak for their technical proficiency, but being able to simplify complex technical jargon is also indicative of excellent clarity in communication in ML.
Moreover, employees should also be able to utilize visualizations like mind maps and various analogies, which can be a great way to decompose complicated information into digestible bits. Using a storytelling approach can also make the presentation a captivating experience for the audience.
In order to evaluate a candidate based on their ability to explain complex concepts, interviewers can ask questions like these:
- “How would you explain a neural network to a non-technical stakeholder?”
- “Can you describe a complex ML algorithm in simple terms?”
Teamwork and Collaboration
It goes without saying that teamwork and collaboration form the pillars of any successful company. As a recruiter, one must ensure that the shortlisted candidates are willing to be team players and participate in meaningful collaborations to achieve significant outcomes.
Teamwork AI can include working alongside interdisciplinary teams, sharing important knowledge within a team or with other teams, collaborating with various teams to solve challenging crisis situations, and planning better outcomes. The right candidate will be able to leverage the collective strengths of every individual to work in synchronization and maximize the output. Collaboration skills ML play a big part here.
Candidates must also be open to feedback and be able to work on their issues to improve their efficiency. Additionally, they should be able to communicate any pain points positively and work to resolve any conflicts at once.
As an interview, you can assess these qualities in a shortlisted candidate by asking the following questions:
- Exemplification: “Can you provide an example of a successful team project you were involved in?”
- Conflict resolution: “How do you handle disagreements or conflicts within a team?”
Conclusion
Hiring the right candidate for an AI or ML role is not an easy job! It requires thorough examination, evaluation through interview questions, AI, and getting to know the candidate on a personal level to assess them beyond their technical knowledge.
Our guide covers all the necessary questions and ML hiring tips to ensure an overall evaluation of a candidate before making the final call. These questions are built to analyze their technical skills as well as the much-required soft skills that can help them bag the job! Analyzing a candidate based on these parameters can ensure that you make the best hire for an AI or ML role from within your candidate pool.