You're evaluating job candidates for an AI role. How can you ensure they're the right fit?

To find the right fit for an AI role, start with a clear job definition outlining the essential skills and the specific problems the candidate will be expected to solve. Thoroughly review their portfolio for relevant projects that showcase their technical abilities and problem-solving approach. Use practical skills assessments or coding challenges to gauge their proficiency. Conduct behavioral interviews to understand their thought process, adaptability, collaboration style, and how they handle challenges. Also, assess their potential alignment with your company's values and team culture.

The major aspect of the selection should be - How much open and interested the candidate to learn or upgrade himself/herself. If this fails, everything else is failed. Other than this, yes the tech skills play a role and based on which levels the hiring is connected to one can fix the par.

AI Business Transformation is top priority for most C level execs and boards But the C level execs need to understand enough about how AI + Knowledge can impact their company and industry first Before they can define what the RIGHT profile and role of their AI Strategist and Transformational leader needs to be

To ensure that candidates are the right fit for an AI role, several strategies can be employed. Firstly, a thorough evaluation of their technical skills and knowledge in areas such as machine learning, deep learning, and natural language processing is essential. Assessing their practical experience through projects and previous work can provide valuable insights. Additionally, evaluating their problem-solving abilities, creativity, and adaptability is crucial for the dynamic nature of AI work. Conducting behavioral interviews, assessing their communication skills, and considering their alignment with the company's mission and values can also help determine if they are the right fit for the role.

Defining the role in AI is crucial due to the field's complexity and rapid evolution. It's important to distinguish between needs for research-oriented positions, which may require a deep understanding of machine learning theory, and applied AI roles, which often prioritize practical experience with AI tools and deployment. Additionally, the interdisciplinary nature of AI means that collaboration skills are as important as technical expertise, as AI projects often involve cross-functional teams including data engineers, business analysts, and domain experts

A candidate's portfolio offers a tangible demonstration of their AI competencies, showcasing their practical experience beyond theoretical knowledge. It's essential to assess not just the complexity of the projects but also the candidate's role in them. Did they lead the project, or were they part of a team? What was the scope of their contribution? This can provide insights into their collaborative skills and ability to drive projects to completion. Additionally, the evolution of their project complexity over time can indicate their learning curve and adaptability to new challenges in the AI field.

Portfolios don't tell the full story. The best AI talent is often found via referrals and inside connections. Brilliant minds may lack impressive portfolios if they did groundbreaking work in stealth mode or at companies restricting public disclosure. An unconventional resume could signal the creative genius you're seeking. Don't dismiss candidates just because their prior achievements aren't publicly showcased.

A portfolio would be nice to have but when hiring that is seldom the case as your candidates will typically have their experience from companies working on closed source projects. It will and should be where they do their best work. If it is an external software it is possible to reference it but they are not working in isolation on that product so the usefulness is low on this.

Thoroughly review each candidate's portfolio, including projects, publications, and contributions. Assess the depth and breadth of their AI knowledge and practical experience. A strong portfolio demonstrates relevant skills, problem-solving abilities, and a genuine passion for the field, signaling they are well-suited for the AI role

Look for evidence of 'translational' thinking. Can the candidate articulate the connection between their project and its potential real-world impact? Does their portfolio demonstrate an understanding of moving from technical solutions to solving problems in a business or societal context? This ability to translate AI's potential is crucial, especially in roles that interface with stakeholders less familiar with the technology. It signals a valuable mindset for impactful AI work.

Selecting an AI candidate is akin to recruiting a maestro for a symphony. Test skills like a musical audition, assigning practical tasks to assess algorithmic prowess (AI Developer) or gauging strategic acumen for orchestrating AI solutions (AI Architect). Probe ethical considerations to ensure harmonious practices (AI Ethics Specialist). Assess adaptability with real-world problem-solving scenarios, like a conductor facing unpredictable changes. Just as a conductor seeks the perfect harmony, testing AI skills ensures the right candidate composes solutions that resonate with the organization's goals.

Whenever I am evaluating a candidate for AI roles , I try to assess his/her skillsets in three areas: 1) Logical ability, problem solving- this skill is best judged by asking cases studies or giving a use case and evaluate how candidate is structuring all possible analysis and what kind of algorithms is being suggested. 2) Mathematical/Statistical fundamentals- you can do at various depths in this area according to skillset you need on team but the minimum requirements would be candidate understands what different kinds of distributions are there, basis understanding of central measures, variance, correlation, causation etc. 3) Programming and libraries know how- evaluate this by asking candidate to write a snippet to implement a use case.

Practical challenges during the hiring process can reveal a candidate's problem-solving approach and adaptability to real-world scenarios, which are crucial in AI roles. It's important to ensure the task aligns with the job's daily responsibilities and allows for creativity. This approach not only assesses technical skills but also provides insight into how a candidate may fit within the team and company culture.

Testing the ACTUAL skill is the MOST important step in evaluating a candidate. We use a three step interview process: 1) we have a typical interview where we review and have the candidate explain their work history as it relates to our AI company. We look for three things - experience, niceness and drive 2) then we have a peer level at our company share their work and have the candidate give feedback - what is the goal of the work, what’s unique and where can it be improved. Always where can it be improved 3) And finally, we give them a “homework” and ask them to present it back to us. This last two steps is the best way to evaluate how they think and work

A skill tests might be something if you are in doubt of their skill level after a technical interview. It needs to be relevant and on the point and quite time consuming to make and maintain.

During the interviews, we evaluate three things: 1) their expertise - so they know what they say they know AND do they have the right skills to be an A+ team member? I’ve learned it’s better to keep a spot open than fill it with less than an A+ player 2) their niceness - are they they the type of person you wouldn’t mind being stuck in an airport with, when there are delays. This is a simple ways to assess value, trust and respect 3) are they driven - not in an obsessive fashion but are they driven by curiosity and are they on that fast learning path. They is important, because teams will always face challenges - and curiosity and drive is what overcome these challenges over and over again

Interviews are more than just evaluating skills; they're a deep dive into who a candidate truly is. To understand their personality, motivations, and how they fit with your team, ask questions that uncover their story. Explore their past experiences, aspirations, and the hurdles they've overcome. Challenge them with scenarios that gauge their creativity, knowledge, and eagerness to learn. Delve into questions that reveal their core values, ethical compass, and vision for the future. Listening attentively, asking follow-up questions, and sharing feedback can turn an interview into a meaningful conversation. This is not just about finding the right fit for the job but also about building a connection and trust.

Let the candidate talk about their experiences and their views on the different subjects such as the coming job that you have presented a bit more of and their last job. If they talk bad about their current employer they need to explain why and what made them feel that way. It might be reasonable but it might also not be and that it the tale you should be interested in. It will show their mentality and might be a warning sign.

Interview: Finding the Right Fit 1. Growth Mindset: Ask about failed AI projects and lessons learned. 2. Impact Focus: Do they care about the results of AI, not just cool tech? 3. Situational Insight: Pose scenarios, like explaining AI to a resistant stakeholder, to see their approach. 4. Curiosity Beyond the Job: Do they follow AI developments outside their specific area? 5. Ethical Dilemmas: Present a scenario where AI is profitable but has questionable impact. Key Mindset: Two-Way Street: Ensure you're selling them on your mission as much as they're selling themselves.

During interviews, ask probing questions to gauge candidates' AI knowledge depth, critical thinking abilities, and problem-solving approaches. Insightful questioning uncovers their technical expertise, creativity, and passion for the field, enabling you to identify the ideal fit for the AI role's demands and your organization's needs

Checking references isn't just about ticking a box; it's an opportunity to gain a fuller picture of the candidate's abilities, work ethic, and fit for your team. Reach out to those who've seen them in action, preferably in roles or projects related to AI. Inquire not just about their technical prowess but also about how they work with others, their communication style, and their capacity for growth and adaptation. These conversations can be enlightening, offering a glimpse into the candidate’s professional demeanor and reliability. Checking references is your safeguard, ensuring the person's accomplishments and character align with your expectations and your team's culture.

References: Uncover Work Style 1. Go Beyond Skills: Ask about how they handle tight deadlines, unclear goals, and project changes. 2. Collaboration Clues: Do they work effectively in teams and share knowledge? 3. Red Flags Matter: Vague praise or hesitation from a reference can be revealing. 4. Fit with Your Culture: Does the reference describe a work style aligned with your team's pace and approach? Key Mindset References should either confirm your positive impression of a candidate or raise concerns to investigate further.

This is quite standard to do but also crucial as you might base your decision on a false reference. Here check not only references as in people but also their references to open source projects that they claim to have worked on.

Thoroughly check candidates' professional references to gain valuable insights into their skills, work ethic, and achievements. References can corroborate technical abilities, validate claims, and provide a well-rounded perspective, helping ensure the candidate is genuinely qualified and the right fit for the AI role

Go deeper with your reference questions. Avoid generic inquiries like "Would you rehire this person?". Instead, frame your reference questions around the specific challenges and needs of your AI role. If collaboration is key, delve into examples of how the candidate contributed to teams in the past. If your project involves sensitive data, ask about their approach to ethical data handling. Tailored reference checks surface the insights most relevant to your decision-making.

To ensure candidates for an AI role are the right fit, scrutinize their technical expertise through reviews of their work on AI projects and hands-on technical assessments. Gauge their problem-solving skills and creativity by discussing how they've navigated past challenges. Assess soft skills like teamwork and adaptability, crucial for thriving in dynamic AI environments. Finally, align their career aspirations with your organisation's opportunities to foster long-term engagement.

Look for Long-Term Potential: 1. Prioritize Adaptability: Specific AI tools change. Find candidates who demonstrate the ability to learn quickly. 2. How Do They Stay Current? Self-driven learning (reading papers, courses) is a good sign. 3. Problem-Solving Over Technique: Understanding core principles aids in adapting to new tools. 4. Domain Knowledge Blend: AI experts who understand your industry's problems are highly valuable. 5. Beware of Hype: Buzzword focus may suggest a lack of foundational understanding of AI principles.

Don't only look for skills. The ideal candidate should not only have the technical skills required but also be a good team player, have a strong ethical understanding of AI applications, and be a continuous learner. This holistic approach helps ensure that the candidate is not only capable of performing the job today but can also evolve with the role and contribute to the organization's growth in the future.

Show me your workflow, AI tools that you use, your prompts, how you research, and what your KMS is, show me that you are as enthusiastic about AI as I am, and then we can evaluate relevant skills for the position. Curiosity, continuous education, and strong ethics are really important in this AI world.

A key factor in assessing AI candidates, often overlooked, is their continuous learning and adaptability. Beyond technical skills, candidates must show commitment to ongoing learning and ability to adapt to AI's rapid changes. Willingness to gain new knowledge, keep up with tech advances, and update skills is crucial for AI professionals to remain relevant and effective over time.