What skills to look for when hiring for machine learning & AI roles By: Alana Reich May 13, 2024 Estimated reading time: 7 minutes. Machine learning (ML) and artificial intelligence (AI) are continuing to revolutionize industries by automating processes, empowering data-driven decisions, tailoring customer experiences and ultimately making our lives a whole lot easier. This growing importance of AI innovation and the transformative power of different technologies has, in turn, created an increasing demand for professionals skilled in machine learning and AI. But with the growing need for machine learning and AI experts also comes the task of sourcing top-tier tech talent who not only have the technical programming skills, but who also possess vital interpersonal skills. After all, much of the discussion surrounding artificial intelligence often involves the age-old debate of whether robots will truly take over every profession; for now, at least, ML/AI roles still require the unique, emotional nuances of humanity. With this in mind, it’s clear that prioritizing a blend of both hard and soft skills is the key to success when looking to launch tech talent acquisition. When it comes to the technical proficiencies, Lighthouse Labs’ immersive Data Science Program equips students with the foundational skills needed to succeed in a machine learning or AI career. This program also arms students with soft skills that flourish in such a fast-paced environment, including adaptability, problem-solving, and a growth mindset. Keep reading to learn more about the in-demand data science skills that our graduates come out with, as well as the accompanying non-technical traits to look out for when hiring. Understanding machine learning/AI roles Before we get into the top attributes to prioritize when looking to hire machine learning/AI professionals, let’s first define what these roles are and what their significance is in today’s evolving tech landscape (P.S. It's also important to note that the terms ‘machine learning’ and ‘AI’ are not synonymous. Check out this post on our blog to learn more about how the terms differ). There are many different kinds of machine learning and AI roles out there, including titles such as machine learning engineer, big data engineer, data engineer, AI engineer, AI research analyst, and so on. The responsibilities of such roles can include (but are not limited to) designing and implementing ML algorithms, analyzing and mining data, and developing AI solutions. These roles are truly at the forefront of innovation and are tasked with finding ways to enhance and improve the ways in which businesses operate. But as businesses continue to leverage technologies to stay current in our increasingly demanding and fast-paced society, the role of machine learning and AI professionals has also evolved to become one that is inherently underscored by ethical considerations. Nowadays, there are more concerns about the harm that technological advancements might have, which means that ML and AI professionals have to reflect on the potential consequences and impact that their work has on our greater society. Essential technical skills Now that you have a basic understanding of what machine learning and AI professionals do, you also need to know what to look for when hiring in this area! If you’re not an expert in this field, you might not be familiar with the practical skills that are required for such roles. Critical technical knowledge for ML/AI professionals includes expertise in: Python programming: Python is one of—if not the most—popular programming languages that exists, so it is vital that all prospective machine learning/AI candidates are proficient in this coding program. Python is a high-level, general language that is used for many tasks, such as building software and automating processes, so knowing how to use Python for AI is definitely a valuable and sought-after skill to have. When assessing potential candidates for ML/AI roles, keep an eye out for applicants who may have completed the Data Science Program at Lighthouse Labs: the Program provides essential data science skills and even has a dedicated module that explores statistical modelling with Python. The Program is available in a 12-week full-time Bootcamp or a 30-week part-time Flex format, but no matter how students choose to complete the Program, the outcome of fundamental knowledge is the same. If you are looking to gain Python skills, Lighthouse Lab’s free Python Crash Course is beginner-friendly and only takes about 30 hours to complete. The course takes students through data science fundamentals such as variables, strings, lists, looping, iterating, boolean logic, and flow control, and also builds on foundational skills in probability and statistics by teaching students how to build different games using Python. Data analysis and modeling skills: It’s also important for machine learning/AI professionals to have a good understanding of data structures, statistics and ML algorithms. Familiarity with machine learning libraries, like Python’s Pandas and NumPy, as well as the ability to clean and transform data using languages like SQL, will be assets to look out for when sifting through applications. Software engineering principles: Another aspect of ML/AI expertise is rooted in principles of software engineering. This includes experience in things like version control, testing, and continuous integration and deployment. Candidates should also be skilled in containerization and should be familiar with using platforms like Docker. Crucial soft skills Although technical skills are certainly important for machine learning and AI roles, the presence of strong interpersonal competencies should not be overlooked. There are several crucial soft skills to look out for when hiring for tech: Adaptability & continuous learning: In the same way that AI technology is constantly evolving and improving to meet the needs of our ever-changing world, machine learning and AI professionals must also be continuously updating their skills in order to stay current. Candidates who are adaptable and have a life-long thirst for learning will thrive in the fast-paced and unpredictable (yet exciting!) nature of this industry. Attention to detail: Being meticulous is definitely an important trait to have in all lines of work, but in the context of AI, being ‘detail-oriented’ has much more significance than the mere ability to catch a small typo. For example, in the development of autonomous self-driving vehicles, even the tiniest of errors in ML algorithms can lead to horrible outcomes for drivers, passengers or pedestrians. This is why machine learning and AI professionals must have a razor-sharp sense of precision and accuracy. Communication: Although machine learning and AI professionals undoubtedly serve a crucial role in all kinds of businesses, stakeholders who lack expertise may sometimes fail to understand their significance. This is why candidates who can effectively translate technical details into layman’s terms will ultimately be the ones who come out on top: having the ability to successfully communicate with stakeholders will ensure that the business value of AI roles is clear. Time management & teamwork: Like in many other fast-paced areas of work, prospective candidates should be comfortable with AI project management, working under tight deadlines and juggling multiple tasks at once. With the rapidly changing nature of artificial intelligence, for example, it’s important that ML/AI professionals are able to implement new models or algorithms quickly in order to maximize efficiency and reach business objectives. As well, learning how to effectively work with colleagues and other stakeholders is another key skill for ML/AI professionals to possess. Although these roles engage in a lot of independent work, they also often work on larger teams, so finding candidates with a team-player mindset is the way to go. A graduate from our Data Science Program, Carly, said that she actually had to collaborate with a data science team of a major-league soccer team while working on her final project and that she learned to navigate through different “problems and opportunities” in order to make her project a success. Problem-solving: Whether working independently or on a team, being able to find innovative solutions to challenges is another vital trait that ML/AI professionals need to have. When working with complex data sets and automated algorithms, for example, it is common for errors and bugs, like missing or corrupted data, to pop up. This is why the right candidate needs to be a solution-oriented person who is not afraid of jumping head-first into unexpected challenges. Machine learning in action With a good balance of both hard and soft skills, machine learning and AI professionals will be prepared to apply their expertise in the real world. Another recent graduate from Lighthouse Lab’s Data Science Program, Oliver, shared that he landed his first role in machine learning shortly after completing the intensive Bootcamp. During the Program, Oliver completed a computer vision project, which ultimately served him well in the role he landed as it was in the computer vision field. “The knowledge gained and feedback received from that project was directly applicable to the role I went on to do,” said Oliver. Seeing from the above example, it’s clear that there are a variety of niche fields, like computer vision, where very specific kinds of machine learning or AI expertise are needed. Other specific fields of data or computer science might include game design, where AI innovation is rapidly changing the field as we know it. Interview questions for ML/AI roles Now that you know what hard and soft skills to look for in prospective applicants, how exactly do you assess these skills in an interview? Well, there are a variety of strategic and targeted questions you can ask candidates to get a better sense of both their technical expertise and interpersonal instincts: Technical questions: How would you handle an imbalanced data set? Explain the difference between deep learning, artificial intelligence (AI), and machine learning. How do supervised and unsupervised learning differ? What are the advantages of using a containerized approach for deploying ML models? Interpersonal questions: Tell me about a time when you had to pitch an idea/give a presentation to a stakeholder. How do you react when you have to pivot or adjust your strategy to complete a task? How do you prioritize work when balancing multiple projects? Share an example of when you made a mistake at work. Building your machine learning/AI team Looking to take the first step to finding top-tier talent? Consider leveraging Lighthouse Labs’ Career Services so you can connect with tech candidates that fit your business’ unique needs. Our experienced Career Services team loves to play matchmaker and can provide you with a curated pool of talent to help you find the right fit for any tech role. With such high demand for ML/AI roles, however, it can definitely be a challenge to find the right external candidate. If you’re looking to fill tech roles in a more cost-effective manner, Lighthouse Labs also offers off-the-shelf and custom, tailored Corporate Training Programs to help your internal employees upskill and reskill in tech. Not only is training internally a great way to relieve the workload on core teams, but it also promotes diversity, equity and inclusion by encouraging employees to uncover hidden talents. No matter which route you take to fill your technical skills gap, it’s evident that the right candidate not only needs to be an algorithm-loving Python wizard but also a detail-oriented, problem-solving team player. It’s clear that machine learning and AI roles are a pivotal part of any business, so it’s in your organization’s best interest to invest in hiring and training skilled professionals. Want to bridge the tech talent gap with Lighthouse Labs? Sign up for our newsletter to get the latest industry insights.