Top Skills and Qualifications to Look for in AI and Machine Learning Candidates
In the last few years, AI has grown exponentially, bringing forth a new way for people to work. Integrating AI into the workforce aids humans in making better decisions by providing insights backed by data. With machine learning and AI’s boom, there has been a 12% increase in AI and ML job openings in the last year alone. As more companies are opening their doors for AI and ML roles, finding the right skills can help candidates land their dream positions.
Essential Technical Skills
When it comes to looking to upskill in artificial intelligence and machine learning expertise, candidates can choose from a wide range of skills, from programming languages to big data to security.
Proficiency in Programming languages:
Upskilling in programming languages can give candidates entry to top-tier jobs in artificial intelligence. They can choose from Java, JavaScript, C++, R, and Python. Among these programming languages, Python is the most widely used. With straightforward syntax, the programming language is easy to learn, read, and maintain for beginners. It is also helpful for scripting and automation.
Following Python, Java’s object-oriented programming language is another skill that can benefit candidates. Using Java allows developers to scale and maintain code. Another tremendous open-source programming language is R for ML, which is easily compatible with Linux, Windows, and Mac.
Experience with Machine Learning Frameworks
Machine Learning, a subset of artificial intelligence, allows developers to create unique algorithms that can learn from raw data and predict outcomes based on the available data. Honing skills such as ML can help candidates land AI and ML roles as they have become increasingly popular, given the industry adoption of Big Data.
Google Brain Team developed the machine learning open-source software library called TensorFlow in 2015, which has been widely used ever since. With the help of TensorFlow, businesses often build customized smart applications that help in their day-to-day activities, such as decision-making. Similarly, PyTorch or Torch, written in Lua scripting language, is an open-source ML framework used for creating deep neural networks. It is easy to use and provides excellent debugging solutions and simple APIs for developers.
Further, Scikit-learn and Keras, written in Python, are two other ML skills that aid in business operations daily. Scikit-learn has been designed for many machine learning tasks like regression, clustering, and classification. With a built-in algorithm, Scikit-learn provides an easy user interface for small or mid-sized data sets.
Data Handling and Analysis Skills
With the onset of AI, data management has become a crucial role. This is why many companies are looking for individuals with data handling skills and expertise in data analysis for AI. Proper data management can help solve problems such as poor predictions, added friction, inaccessibility, and biased and incorrect results. While labor-intensive, data handling skills include cleaning, integrating, and organizing data to help maximize the efficiency of Artificial intelligence models. In the role of a data manager, responsibilities such as assisting data scientists and employees are also included. Moreover, effective data management can help with scalability, which, in turn, can help models handle larger datasets. Additionally, data management helps with the reproducibility of AI, which has always been a challenge due to its complex algorithms and black-box models.
Important Soft Skills
AI and Machine Learning professionals should sharpen analytical and problem-solving skills to define the future of AI. Without rationality and unbiased opinions of professionals tutoring AI, AI models can start providing biased outputs.
Problem-Solving and Analytical Thinking
Candidates require these soft skills to build algorithms that work with big data, search for trends, and predict results. Often, this starts with setting a clear goal, executing it, and fine-tuning algorithms for optimal performance of the AI models. In the case of image recognition, optimizing deep learning models often includes an in-depth understanding of cognitive biases and optimizing LLMs with objective data to ensure efficiency and improve hallucinations within the AI. This is why one of the most essential ML soft skills is dealing with ambiguity and having an objective mind.
Effective Communication
Soft skills such as effective communication are essential for candidates who wish to work in the AI and ML industries. As the AI industry is multidimensional, interpersonal collaboration between data scientists, engineers, and managers is a soft skill that will be beneficial to professionals. Communication of technical knowledge helps navigate conversations between people from tech backgrounds and any non-tech stakeholders that might be involved which helps in better technical explanation. By promoting greater opportunities for collaboration, which strong interpersonal skills unlock, the adoption and development of creative ideas are likely to boost successful outcomes. Ultimately, it cannot seem easy to balance the multidisciplinary nature of AI and ML projects without these soft skills in place.
Relevant Educational Background and Certifications
As the adoption of AI and ML has increased, certification and educational expertise in the field have also evolved with time. According to a study conducted by IDC, the AI software market is expected to bring about $791.5 billion in revenue by 2025, with a Y-O-Y growth of 18.4%. In a promising industry like AI and ML, it is not a surprise that many aspirants are choosing to opt for AI degrees and ML certifications.
Relevant Degrees
Candidates can take up Computer Science, Mathematics, Engineering, or Data Science degrees to enter the AI and ML industry, as all of these degrees discuss AI in the basics. However, walking on the traditional Bachelor’s may only be for some. Any courses that cover the basics of AI, such as programming languages, algorithms, and statistics, can be relevant for AI. However, for in-depth knowledge, often a Bachelor’s degree is necessary, followed by a Master’s and a PhD in Machine Learning, AI, and Data Science.
Industry Certifications
As mentioned, not all candidates in the AI and ML space start with a bachelor’s degree. Many candidates begin as marketers and designers and in other professions by choosing to upskill in AI certifications and ML credentials. Certifications like AWS Certified Machine Learning Google Professional Data Engineer can validate a candidate’s ability to design, implement, and deploy AI models. Further, these certifications can bolster formal education and help a professional get a better foothold in the industry. These sets of education and certifications can equip professionals with the right tools to have a competitive edge over other candidates.
Conclusion
Having a mix of proper educational background, aided by certificates along with soft skills, can help a candidate ascertain themselves in the AI and ML industry. In such a dynamic industry, a balance of skills is preferred over one. However, finding the proper role can often take time and effort, and having a recruiting agency can help along the way.
If you are looking for opportunities or hiring AI candidates, contact AI & ML Recruiting Agency so we can help you in your search!