What Companies Look for in ML Candidates: Technical and Non-Technical Skills

When it comes to job interviews, companies have two main objectives. Firstly, they need to determine whether a candidate has the necessary skills and knowledge to do the job. Secondly, they want to assess whether they can provide the candidate with an environment in which they can excel. In this article, we’ll explore the technical and non-technical skills that companies look for when hiring for machine learning (ML) roles.

Technical Skills

Software Engineering

To train and deploy ML models, it’s crucial to have a strong foundation in software engineering. Familiarity with algorithms, data structures, time/space complexity, and scalability is essential. You should also be proficient in Python, Jupyter Notebook or Google Colab, NumPy, scikit-learn, and at least one deep learning framework. It’s helpful to have knowledge of performance-oriented languages such as C++ or Go as well. Check out BestPracticer’s list of engineering skills for different levels of proficiency.

Data Cleaning, Analytics, and Visualization

Candidates who know how to collect, explore, and clean data have a significant advantage in the ML industry. It’s also important to be proficient in creating training datasets, dataframe manipulation (pandas, dask), and data visualization (seaborn, altair, matplotlib, etc.). SQL is useful for relational databases, and R is popular for data analysis. Familiarity with distributed toolkits like Spark and Hadoop is a bonus.

Machine Learning Knowledge

To stand out as an ML candidate, you need to have a deep understanding of ML concepts beyond buzzwords. It’s important to explain every architectural choice you make and be able to evaluate potential solutions and debug your models.

Domain-Specific Knowledge

Having knowledge relevant to the products of the company you’re interviewing for is critical. For instance, if the company works in the autonomous vehicle industry, you should know about computer vision techniques and tasks such as object detection, image segmentation, and motion analysis. Similarly, if the company builds speech recognition systems, you should be familiar with mel-filterbank features, CTC loss, and common benchmark datasets for speech recognition.

Non-Technical Skills

Analytical Thinking

Employers value candidates who can systematically approach complex problems and break them down into manageable components. Analytical thinking is a critical skill, especially for junior roles. Python can be taught in a few weeks, but it takes years to teach someone how to think.

Communication Skills

Real-world ML projects involve various stakeholders with different backgrounds. It’s crucial to communicate technical aspects of your ML models to people involved in the developmental process who don’t have technical backgrounds. Being unable to articulate your ideas can be a dealbreaker, even if your ideas are brilliant.

Experience

Employers consider whether candidates have completed similar tasks in the past and can generalize from those experiences to future tasks. Experience is critical in the ML industry, where empirical observations often lead to improvements. In this context, experience is different from seniority.

Leadership

Companies look for candidates who can take initiative and complete tasks without constant guidance. You don’t need to know how to do all components on your own, but you should know what help you need and be proactive in seeking it. This quality can be evaluated based on past projects, such as whether you took initiative in school or in your previous jobs.

Conclusion

Technical and non-technical skills are essential for success in ML roles. Candidates who possess a strong foundation in software engineering, data handling, and ML concepts, as well as non-technical skills such as analytical thinking, communication, experience, and leadership, are highly valued by employers. By highlighting these skills in your resume and during interviews, you can position yourself as a highly qualified candidate for ML roles.