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10 data scientist skills and how to assess them when hiring a data specialist

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Data scientists can give your business a huge competitive edge. For example, what if there are useful sales, customer service, or productivity insights you haven’t noticed?

These scientists work with companies to understand these insights, measure marketing performance, gauge customer interests, improve efficiency, and make predictions.

Without data scientist skills, your workforce is running on guesswork. Collecting and analyzing objective data is the key to making better business decisions and forecasts.

Below, we explore what a data scientist's skill set looks like and show you how TestGorilla talent assessments can help you find the best talent because resumes don’t account for the technical skills a data scientist needs.

10 fundamental data scientist skills and traits

Data scientists are skilled professionals who work with complex datasets to solve problems and make predictions. 

Their work involves arranging data, testing hypotheses, and delivering presentations. Given the highly technical nature of their work, specialist skills are crucial. However, there are non-technical skills to consider, too.

Before you hire data scientists outright, let’s explore ten data science skills you should prioritize testing for.

1. Mathematical ability

Mathematics plays a major role in the field of data science. Data scientists have to extract meaningful insights from complex data sets. Therefore, mathematical skills form the backbone of this process. 

A solid understanding of mathematical concepts such as probability and statistics helps data scientists design models, perform analyses, and interpret data patterns. 

Some data analysts, for instance, use mathematics to predict which healthcare treatments to offer individual patients.

Beyond general math, linear algebra is important to data scientists. Experts often use it when working on large data sets to build models and understand underlying relationships.

2. Predictive modeling

Predictive modeling enables data scientists to use historical data to build models that can accurately predict future outcomes. 

A scientist might create predictive, logistic regression models that forecast business outcomes. For a finance company, a predictive question might be, “Will investing in this asset risk X amount of capital?

By uncovering patterns and trends in data, predictive modeling helps scientists to identify potential risks and opportunities. 

According to one McKinsey case study, a company used predictive modeling to analyze resources and avoided missing their delivery schedule, a delay that would have cost the firm a $350m contract.

With the power of predictive modeling, data scientists can anticipate customer behavior, forecast sales, detect anomalies, and personalize customer experiences.

3. Coding and programming skills

Coding and programming skills are indispensable for data scientists. Python, in particular, is a valuable language in their toolkit. 

Data scientists use coding and programming for efficient data manipulation and analysis to derive insights. 

Writing clean, well-structured code and utilizing something like natural language processing enables data scientists to automate and facilitate their repetitive tasks, so strong computer science skills save them time on writing, editing, and re-testing elements repeatedly.

However, it pays for scientists to learn how to read and write in languages other than Python, such as C++, SAS, or SQL.

Python is only the third most-demanded programming language worldwide, behind Java and Javascript:

A chart showing the 15 programming languages demanded the most by recruiters worldwide in 2023.

So, it makes sense to test applicants on any software engineering languages you use in-house, such as with a test on data analysis using R. Other frameworks, packages, or libraries to be on the lookout for in your data scientist’s skill set include:

  • Apache Hadoop

  • Spark

  • MongoDB

  • NumPy

4. Machine learning

Around 35% of businesses use machine learning and artificial intelligence, with 42% more interested in exploring the technology. It’s one of the most sought-after data science skills.

Data scientists use machine learning to create algorithms and models to learn from big data. Doing so helps them make accurate predictions. 

Machine learning algorithms enable data scientists to solve intricate problems that don’t have clear rules or patterns. They could, therefore, discover valuable information that traditional analysis overlooks.

By analyzing historical data, data scientists use machine learning techniques to:

  • Find hidden patterns

  • Spot unusual occurrences

  • Group customers

  • Suggest personalized content

  • Improve processes

  • Automate tasks 

Pandas and TensorFlow are especially key machine learning packages for data scientists, as they run in Python.

5. Neural networks

Neural networks, a type of machine learning, mimic the human brain to process and understand large amounts of data. They’re great at recognizing images and speech, understanding language, and finding patterns. 

Neural networks are superb for data scientists to have in their arsenal. In a business context, they’re useful for handling complex data and solving difficult problems, helping data scientists extract valuable insights from diverse sources.

Neural networks are common in financial forecasting, e-commerce personalization, and fraud detection.

As an example, a neural network can learn to spot hidden signs of fraud in consumer finance, helping banks better protect their customers.

6. Database management and data wrangling

One of the most important skills for a data scientist is understanding how to pull, arrange, and categorize data sets for others.

Wrangling and management skills help data scientists to clean and centralize information, improving report accuracy.

Expert data wranglers are fluent in multiple coding languages. Coding knowledge helps wranglers arrange datasets more efficiently with custom programming, e.g., to import large datasets and spot missing details.

7. Cloud computing

Data scientists must know how to access, control, and store information across platforms such as those in the cloud.

Around 46% of companies use cloud platforms for their HR approach. Therefore, it pays to hire data scientists who use these platforms, which include things like Amazon Web Services (AWS).

8. Communication

Data scientists claim their work splits around 80/20 – 80% data research and 20% communication. However, communication forms the backbone of a data scientist’s work. They have to communicate their findings and recommendations to others regularly. 

That includes non-technical audiences such as executives, clients, and stakeholders. 

Effective communication skills help data scientists translate complex data-driven insights into meaningful and actionable information. This information can then guide the decision-making in your business. 

Strong communication skills also improve collaboration and teamwork. That means data scientists can work more effectively with domain experts, data engineers, and business analysts. 

9. Data visualization

Data scientists must show their findings in charts, graphs, and other formats for various audiences. Unstructured data can be difficult for other team members to understand, hampering the decision-making process.

By presenting their findings clearly, concisely, and compellingly, data scientists influence strategic planning and drive change. 

No one tool covers all bases regarding effective data visualization. Therefore, it pays for scientists to have skills in tools such as Microsoft Power BI, Tableau, ChartBlocks, and Fusion.

10. Critical thinking

Critical thinking is an important skill for data scientists due to the nature of their work. It helps them analyze complex problems, evaluate evidence, and make informed decisions based on logical reasoning. 

Critical thinking is one of several data scientist soft skills that help experts:

  • Question assumptions

  • Identify biases

  • Uncover hidden patterns within datasets. 

A talented data scientist can confidently identify which data is biased or unhelpful.

Critical problem-solving helps scientists approach problems from different perspectives and consider alternative solutions. 

By applying critical thinking, data scientists can make sound judgments, validate the accuracy of their models, and mitigate potential risks.

How to assess data science skills and traits

Assessing data scientist skills through experience checks is difficult. For all a candidate tells you they have experience, you’re taking their word at face value, risking a bad hire.

So, consider running a data scientist assessment built around problems employees face on the job rather than arbitrary requirements like a master’s degree or referrals via LinkedIn. We recommend skills-based hiring because it’s a reliable and objective way to judge genuine talent.

Let’s explore how you can use a combination of 15 TestGorilla assessments to make confident hiring decisions.

Data Science Skills test

The Data Science Skills test from TestGorilla assesses candidates’ all-around knowledge of data science concepts.

It covers important areas such as programming skills and theoretical understanding of statistics, machine learning, neural networks, and deep learning. 

To excel in this test, candidates must apply data science principles and techniques effectively to business situations.

A recruit might demonstrate their understanding of confusion matrices, which help to fine-tune classification models, e.g., for predicting product popularity:

 An example question from TestGorilla's Data Science test

Numerical Reasoning test

Our Numerical Reasoning test evaluates candidates' general aptitude for mathematics. The test covers interpreting numbers, understanding number patterns, and manipulating text and tables.

Candidates can demonstrate their mental arithmetic skills:

An example question from TestGorilla's Numerical Reasoning test

Beyond this, there are opportunities to apply knowledge to hypothetical situations, too.

We also recommend running our Fundamentals of Statistics and Probability test. This assessment ensures you hire people who look at probability critically.

Python test

Remember, test purely for the programming languages you use in-house. We recommend the Python Entry-Level Algorithms test as a starting point if appropriate.

Similarly, we offer an entry-level Javascript (coding) test, which asks candidates to test functions:

An example question from TestGorilla's Python test

After testing applicants' Python skills, we suggest reading our guide to the best Python data scientist interview questions.

Similarly, our guide to SAS interview questions gives examples you can use to help ensure you’re hiring the right coders.

Testing these skills can help boost talent retention. LILAB, working with TestGorilla, used such tests – and after six months, 90% of its hired candidates are still on board.

Data Modeling Concepts test

Making accurate models of real-world data is a powerful skill for data scientists. 

Our Data Modeling Concepts test assesses candidates’ capabilities in identifying prerequisites for a data model and creating and working with conceptual, logical, and physical data models. 

Those who excel in this test demonstrate a deep understanding of data modeling principles and their practical implementation, as this example question measures:

An example question from TestGorilla's Data Modeling Concepts test

They show the ability to design efficient data models for company databases, enterprise resource planning (ERP) systems, and various other applications. 

For deeper insights into applicants’ skills, use some of our data modeling interview questions during interviews.

Machine Learning test

TestGorilla’s Machine Learning test evaluates whether the candidate understands core machine learning concepts. 

By choosing data scientists for your team who excel in this test, you get people skilled at training artificial intelligence to sift through data and find useful, actionable insights from machine learning models.

In this test, candidates might need to show their understanding of gender classification, useful for teaching machines facial recognition and demographic sorting:

An example question from TestGorilla's Machine Learning test

Consider testing skills in specific unsupervised learning environments such as Scikit-learn. The more environments a data scientist is skilled in, the more adaptable they are, and the more knowledge they have to develop sophisticated models.

Neural networks

Our Neural Networks test assesses people based on their ability to build and run this technology and solve problems with it.

This test might ask candidates to decide how they’d build an accurate facial recognition program:

An example question from TestGorilla's Neural Networks test

Check out a few more sample questions from this test in our library to get a taste of what’s in store for your candidates.

Data wrangling

You should always test data scientists on organizing and categorizing data. Our Data Wrangling skill test covers extraction, cleaning, integration, and validation.

This test assesses applicants' ability to read raw data and preprocess it for centralization.

For example, they might need to identify how to store data from specific files:

An example question from TestGorilla's Data Wrangling test

Data Analytics in Azure and Google Cloud Platform (GCP)

Our Data Analytics in Azure test ensures applicants know how to store, transform, analyze, and optimize data in the program.

Similarly, our Google Cloud Platform test might ask candidates how to handle data storage in critical scenarios:

Google Cloud Platform test

With a TestGorilla account, you can build assessments to run several cloud tests simultaneously. Take a tour of our demo and try it for yourself!

Communication

Our Communication test enables your applicants to discuss how they work with others and how they interpret certain messages.

Testing collaborative skills is important because, as discussed, data scientists need to work with various people at different business levels, and not everyone possesses their technical knowledge.

You can test how effectively people communicate, such as how they approach others to solve complex problems.

They might, for instance, need to show how they interpret emails:

An example question from TestGorilla's Google Cloud Platform (GCP) test

Data Storytelling

Our Data Storytelling test measures a recruit’s ability to:

  1. Interpret and analyze data

  2. Present said data in different ways

  3. Communicate data insights to different audiences

  4. Translate these insights into concrete business decisions

This test helps identify data scientists who transform the most complex of unsorted data into suggestions for actionable outcomes. 

Our test, for instance, might ask applicants how to edit visualizations to suit a specific audience:

 An example question from TestGorilla's Data Storytelling test

Critical thinking

TestGorilla’s Critical Thinking test puts data scientists in potential work scenarios where they need to solve complex problems with careful reasoning.

Measuring critical thinking for data science roles ensures you hire someone who can analyze and translate data without adding personal bias.

They might need to show their ability to make logical conclusions from complete and factual data:

An example question from TestGorilla's Critical Thinking test

Hire a top data scientist using TestGorilla

Are you ready to hire a top data scientist using TestGorilla’s talent assessments?

Summary: 10 key data scientist skills and how to test for them

Let’s quickly break down the skills required for data scientist roles.

Data scientist skill set

Best TestGorilla assessments to use

Mathematical ability

Numerical Reasoning test, Fundamentals of Statistics and Probability test

Predictive modeling

Data Modeling Concepts test

Coding and programming skills

Data Science Skills test, Python test, Javascript (coding) test

Machine learning

Data Science Skills test, Machine Learning test, Scikit-learn test

Neural networks

Data Science Skills test, Neural Networks test

Database management and data wrangling

Data Wrangling test

Cloud computing

Data Analytics in Azure test, Google Cloud Platform (GCP) test

Communication

Communication test

Data visualization

Data Storytelling test

Critical thinking

Critical Thinking test

How to develop a data scientist skill set

All data scientists should learn different coding and programming languages and experiment with custom data science projects. Ideally, we recommend mastering at least one language, which you can do through options such as:

  • A data science program

  • Online courses and tutorials

  • Programming boot camps

At the same time, those wanting to pursue a data science career should develop soft skills that are transferable from role to role. For instance, they could develop communication skills by working with others and learn how to tell stories with data by experimenting with visualization.

When hiring data scientists, test for these communication skills alongside technical proficiencies. For example, you might find a recruit is exceptional with neural networks, but falls short on soft skills.

You could then prioritize communication development during training and measure progress with talent assessments along the way.

Build a data scientist assessment and hire talented programmers

Finding the best data scientists isn’t easy when you’re just relying on resumes and experience.

Test for data scientist skills and let your applicants and internal hires prove they have the technical knowledge and interpersonal skills to break down your data effectively.

With TestGorilla assessments, you can test for data science skills and easily rank which candidates show the most promise.

It all starts with our demo – let us show you how our tests work in practice.

We also recommend taking a product tour before registering your own TestGorilla account. 

Then, set up a free forever plan and take us for a test drive.

Data science assessment FAQs

Let’s answer some quick questions about essential skills for data scientists.

What is a data scientist skills assessment?

A data scientist assessment examines technical proficiency, machine learning expertise, and problem-solving skills. It includes practical tests, case studies, coding challenges, and interviews. By conducting a data science assessment, you can select candidates with the right blend of technical expertise, analytical thinking, and subject knowledge.

Why assess the skills of data scientists?

Data scientists can help your business stand out from the competition. It’s important to accurately evaluate data scientists’ skills to hire the best candidates to help drive your business forward. By analyzing data, they uncover valuable trends, spot new opportunities, and help you outperform others in your industry. 

You can easily assess these skills for data scientists using skills testing.

What skills are needed to be a data scientist?

  • Statistical analysis

  • Mathematics

  • Numerical reasoning

  • Data wrangling

  • Machine learning

  • Data visualization

  • Programming

These are data scientist hard skills. However, soft skills such as communication are equally important in a data scientist’s skill set.

What are some important data scientist soft skills?

Critical thinking and communication are useful soft skills required for data scientist roles. For example, data scientists must know how to deliver their findings to different audiences. Critical thinking helps data scientists evaluate complex information with logic and reasoning. Therefore, they can easily validate data by asking questions and testing their models.

Are programming skills required for data scientist roles?

Yes – data scientist skills such as programming and coding help experts read, interpret, and use complex data. Data scientist hard skills, such as programming, help specialists to automate certain tests and conditions, making their work more efficient. Coding can help scientists fix problems and share "patches" across their companies.

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