If you have ever spoken to a professional data scientist, you’ll realize that they may look like individuals who only specialize in analyzing data but go more in-depth, and you’ll find a whole other story about them.
Data scientists possess many skills, and it is crucial to have them in order to become successful data scientists.
So, what are really the most important skills to look out for when hiring data scientists, especially in 2022?
Let’s dive in and find out in this article!
What method of hiring should you use with data scientists?
The methods of recruiting today are changing rapidly. Traditional recruiting methods aren’t popular anymore and are near death. Collaborative hiring has begun as the new trend for recruiters.
Collaborative hiring is a method of hiring that includes your team in the hiring process. The collaborative hiring process includes the selecting, interviewing, and final decision-making phase.
It’s a suitable method for hiring data scientists since recruiters need to pay in-depth attention and be selective of which data scientists to hire. The collaborative hiring method involves all team members, from the hiring managers to other team members who will meet up with candidates. In other words, this allows the candidate to get to know who they will work with.
Overall, collaborative hiring is suitable for all industries, especially data scientists, as it seeks to get more voices from others, set up a high-quality hiring process, and have a deep understanding of who will join the team.
Top 10 skills to look for in data scientists
Especially if data scientists work for enterprise companies, they will most likely be required to know Java or learn it. Java is considered one of the most common languages in large enterprises.
In general, data scientists go through many experiments while working, so this also means that they have to change their programs rapidly while undergoing these experiments.
Moreover, when hiring a data scientist to assess their Java coding abilities, give them a java coding test to undertake. This way, you can know if they have the necessary knowledge of Java for the job.
Python is undoubtedly one of the most popular programming languages in the data science industry. Python is a programming language that can handle almost everything, from building a website to data mining and much more; it’s all in one language, so to say.
Moreover, nearly 50% of people worldwide use Python and is considered to be the #1 most used language amongst them.
Furthermore, for data scientists to be better at what they do, they should know Python as it can solve many problems and it even has data analysis libraries that are used for easily manipulating data, visualization, reading, and more.
3. Able to write SQL queries
A data scientist who knows how to write SQL queries is highly demanded. You may be asking why? Here are the following reasons:
- Flexibility: data scientists who can do more things than only modeling data are loved by companies. These data scientists can improve data insights, build more in-depth reports, and more.
- Independence: data scientists who can write SQL queries for projects that you set up also save you time and money instead of hiring someone else to do it.
4. SQL databases
SQL, in other words, is used to communicate with databases and is the standard language for standard database management. Data scientists can use SQL to read and receive data from another database or even upload new data.
Additionally, SQL is used for data manipulation as well, and the best part about it is that you can manipulate the data whichever way you like. Creating a new SQL query is usually the first step of evaluating any sequence.
5. Probability and statistics
The use of probabilities and statistics is a must-have for a data scientist. These include knowing descriptive statistics such as the median, mean, mode, standard deviation, and variance. Then, we move on to hypothesis testing, CLT, probability distributions, and much more.
Nobody ever said it was easy analyzing data, but a data scientist can’t ever set foot in this industry without knowing about probabilities and statistics.
6. Communication and collaboration
A data scientist may build many great things, but if they can’t communicate the values they are providing, it may just be useless. Moreover, data scientists who can’t communicate and collaborate won’t get as far as they should.
You should seek to hire data scientists who can communicate (storyteller) how they came up with the insights and models. It’s like telling a story and connecting all the dots for people to understand the whole picture of it. Clarity is above all!
Sadly, communication in the technical world is undervalued, which isn’t a good sign. Good communication is what separates a professional from a junior.
7. Data analysis, manipulation, and visualization
The difference between a great project and an average one is the ability to undertake data manipulation, where the data scientist transforms it for further analysis afterward. Even though data manipulation may take some time, it is worth it. It’ll help data scientists make better decisions and analysis with their data.
Data analysis can be done using Python, SQL, Microsoft Excel, and more. Furthermore, a data scientist needs to know how to conduct and separate a professional data scientist from a junior one.
On the other side, we have data visualization which is a graphical representation of data through the usage of charts, infographics, maps, and much more. As businesses continue to increase their usage of data visuals, it’s becoming an essential skill to have for data scientists. Additionally, what is great about visuals is that they are easy to understand even if you are trying to explain something to a person who has a more basic understanding of data science.
8. Machine learning
Machine learning is a branch of AI and isn’t something easy to be proficient in, and that’s why very few data scientists have excellent knowledge of it. However, machine learning helps data scientists analyze large amounts of data using data-driven models and algorithms, which significantly reduces the overall workload of a data scientist.
You are getting to a whole other level if you’re thinking of hiring a data scientist with an advanced machine learning knowledge that includes natural language processing, recommendation engines, and more. However, remember that a data scientist who knows about machine learning is part of a small group.
9. Explanatory models
There are mainly two models that a data scientist can build: a predictive model and an explanatory model.
Explanatory models are created by using regression models. Moreover, these regression models provide useful statistics for deeply understanding relationships between variables.
Explanatory models are undervalued and not given enough attention as needed. They are beneficial and deeply distinguish how good a data scientist can be. We recommend you consider it when looking for the overall skills of a data scientist.
10. A/B testing
A/B testing is experimenting and comparing two groups to see which one performs better. It’s widely used in many industries and a requirement when undergoing experiments.
Without a doubt, A/B testing is incredibly practical and allows you to make significant improvements and changes over time. In short, it’s an essential skill that data scientists should possess, and to say the least, there shouldn’t be any data scientists or even professionals in other industries that can’t undergo A/B testing.
Wrapping it up
Well, that’s about it for this article. These were our top 10 skills that we recommend you look for in a data scientist. Data scientists are extremely talented individuals and they all have their own strengths in the skills they possess. However, data is only becoming larger each year, so it’s important a data scientist knows how to analyze, process, and manipulate it.