Data science interviews - What we screen for? What we will not?

In the previous post, I stated the ambitions of redesigning the hiring process for three of our data science positions (DA: data analyst, DS: data scientist, DE: data engineer).

We are a large retailer on a journey named digitalization. In this article, we will outline what we screen for in our data science candidate and what we will not.

What we screen for?

We screen for three categories of criteria outlined below: the ability to perform in the job, ability to learn and teach, and alignment with our values.

Ability to perform in the job

Depends on the roles, we look for signs to indicate that the candidate will thrive with us.

  • Problem-solving: We pride ourselves on the ability to move very fast in our organization. To be able to do so, we want our team to be problem-solvers, and I’d rather give them problems than solutions.

  • Visualization and presentation (DA, DS): “A picture is worth a thousand words,” and so is a chart! Our DA and DS have to communicate with stakeholders frequently via dashboards, reports, presentations. Take a look at the visualizations below. A good visualization can help communicate insights quickly and effectively.

An example of mediocre vs. good visualization [(Storytelling with data)]( example of mediocre vs. good visualization (Storytelling with data)

  • System design, Troubleshooting (DE, DS): Our DE will be building data pipelines and other data systems, while our DS will use those systems to build machine learning models and data applications. Therefore they should know about system design and troubleshooting to build scalable, fault-tolerant systems.

  • Code fluency (DE, DS): Coding is an essential skill that our Data Engineers or Data Scientists should possess. Whether they are building data pipelines or data models, they will need to be fluent in their language of choice and quickly translate their solutions to code.

  • Data structures and Algorithms (DE, DS): Having a solid understanding of data structures and algorithms is vital in developing good applications. Especially since we are working with big data, using the right combinations of data structures and algorithms will make or break our work.

Ability to learn and teach


Data science is not new, but thanks to the drastic increase in computing power and popularization of cloud computing, data scientist or data science, in general, have become the sexiest job in the 21st century. Due to only recent popularity, there’s a shortage in the market for data science talents. We don’t expect every hire to be a rock star, but we do hope that they will enjoy learning and teaching their trade.

  • Passion: We look for people who can demonstrate their love for data science. With this, they can self-motivate themselves to learn, grow, and pass on their passion to others. We want our team to go to work every day loving what they are doing and help us drive our organization toward a data-driven one.

  • Curiosity: Constantly asking why is essential. It will put them in a better position to learn the job, do it better, and more creatively than a person who lacks it. When curious people fail, they analyze their failure, because they are keen on knowing the reasons, so they can do better the next time. If you show more interest in what you are doing, you indicate that you care and want to learn and progress.

  • Team Work and communication: The problems we are trying to solve are just too big to tackle alone. Our DA, DS, and DE will have to work closely together with the ultimate goal of helping business functions thrive using data. We regularly have standups, brainstorming sessions, presentations, or presentations to stakeholders. We believe that several individuals working together toward a common task or project goal should typically come up with broader and stronger ideas that one person working alone.

  • Business Acumen: We are here to deliver business value! Whether the value is revenue increase or cost reduction or TCO (total cost of ownership) reduction or automation. Understanding the business and what can bring business value to us is the key to success here.

  • English: We want to hire people so they can grow with us, we don’t want them to stay who they are after a couple of years working here. As a result, they must continuously learn new things, and English is a critical skill that enables them to do so. There are many great resources out there, and the industry is changing every day. Without English (or fluency in English), you lose access to a lot of information and will lag.

Alignment with our values

We try not to look for people who are exactly like us to avoid building a homogenous team. But the following values will help assure that the team is heading in the same direction.

  • Love working: Mark Twain said, “find a job that you enjoy doing, and you’ll never have to work a day in your life.” We love what we are doing, and we believe in the company’s ability to add value for customers as well as our ability to add value to the company.

  • Do hard things: Our team should be able to own their product, and we try our best to help them. What we are trying to do is hard and new (at least in our market) and requires a lot of resilence. We look for people who are willing to take on challenges and have experience doing so.

  • Initiate change: Changes can come in many forms. It can be advocating for a new product or feature, define a better process/framework, suggest a different way of doing things, or getting a decision-maker interested in a problem. Initiate a change can not only help you and your team stand out but can also help drive innovation or new initiative.

  • Self-motivated: We want to know that you will be enthusiastic and committed to your work. We also want to know that you will do your best work even without a boss asking you to do so, or the promise of a reward.

  • Have fun: We want to build a workplace where we can contribute to the company and have fun doing so. We want to know that you will also enjoy joining our quest to learn, contributing, and collaborating.

What we will not (screen for)?

The following areas are not negative. However, we believe that focusing on them can add noise to our judgment and even totally unrelated to what we are looking for.

  • GPA: We don’t care if you messed a semester and got a bad GPA. Just because you don’t have an excellent GPA, doesn’t mean you’re not smart or competence. We don’t see GPA as an indicator that you will perform with us.

  • School: A good school also doesn’t mean you will perform better and vice versa. There are a lot of great self-taught people out there. Many great engineers do not study computer science, and many great managers do not have a business background.

  • Prior Experience: Many companies look for people who have work with big companies or famous startups. While some favorable factors are working for them, there are some unfavorable factors too. We think we can get to know you better through our interviews and assignments than looking at what you’ve done before.

  • Specific technology: We don’t look for people who are fluent in some particular technologies. We think that tools are just tools to help you solve your problem. Knowing the concepts, approaches, and methodologies are more important than mastering a specific technology. Tools can be learned if necessary.

  • Friendship: We don’t look for people who can be a friend with us. We are building a team here, not a family. A good working relationship, the ability to collaborate, and mutual respect are sufficient.

We are hiring

Thank you for reading, we are looking for talented data analysts, data engineers, and data scientists to join our team. Please reach out to me at

Credit to the excellent series here, I adopted a lot of their structures and contents.

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