Technical and Non-Technical Data Scientists Skills to Assess Before Recruiting

With Big Data insights becoming critical to most business operations, the demand for data scientists has grown significantly over the past few years.

Whether it's product development, customer personalization, or analyzing new opportunities, businesses increasingly rely on data scientists to drive growth and improve customer satisfaction.

As such, data science has become one of the fastest-growing jobs of the decade. In fact, the US Bureau of Labor Statistics projects data scientists' roles to grow 36% between 2021 and 2031.

But with the emerging number of data scientists worldwide, it has become more challenging for HR professionals to evaluate whether a candidate has the necessary data science skills for the role.

So, what are the critical data scientist skills recruiters should look for when hiring a candidate?

This blog will discuss the must-have technical and non-technical skills for a data scientist role. It will also explore how to assess and employ great data scientists.

What is a Data Scientist? What are their Roles?

A data scientist is an analytical professional with the technical skills to extract and extrapolate insights from unstructured data.

Their work includes gathering, analyzing, and interpreting vast data to explain a phenomenon or solve complex problems.

Data scientists work with various mathematical, computer programming, and statistical concepts. They also use advanced analytical techniques such as machine learning and predictive modeling to develop and test hypotheses.

In business, data scientists usually mine business data to help managers make data-driven decisions. They help:

• Predict customer behavior and market trends
• Analyze financial risk
• Detect frauds and cyber security threats
• Identify new business opportunities, etc.

Businesses employing data scientists usually witness improved customer service, more effective marketing campaigns, robust supply chains, and higher business growth.

The primary responsibilities of data scientists in an organization include the following:

• Identify data sources and gather relevant data for analysis
• Use analytical tools to detect trends, patterns, and relationships between data sets
• Create predictive models to analyze potential business outcomes for different scenarios
• Present and communicate their findings using various data visualization techniques
  
  

Skills Required for Data Scientists

Given the nature of their responsibility, data scientists need a balanced set of technical and non-technical skills to ace their roles. 

Below are the must-have data scientist skills recruiters should look for while hiring.

Technical Data Scientist Skills:

1. Programming Languages

Programming languages serve as the fundamental data scientist skills that help them transform raw data into actionable insights.

All data science professionals should know how to use some programming languages to manipulate data as required.

While the choice of programming language largely depends on the problem at hand, below are some major ones that recruiters generally look for:

• Python
• R Programming
• SQL
• Java
• Scala
• Julia
• MATLAB
• TensorFlow

Among the list, Python and R programming are desired by most corporates, irrespective of their domain.

A 2018 online poll by KDnuggets found that almost 66% of data scientists use Python, making it the number one language for analytic professionals. It is, therefore, essential for data scientists to brush up their Python skills first.

Besides, data scientists would also benefit from using databases to store data in a structured way so they can be retrieved whenever required.

Some popular databases used by data scientists are:

• MongoDB
• MySQL
• PostgreSQL
• Microsoft SQL Server
• SQLite
• Google Cloud BigOqery
• Oracle Database

2. Data Analysis and Visualization

For any decision-maker, seeing data in a clear visual format is easier than viewing large data tables. This is where data visualization becomes essential.

Visualization allows business managers to get an instant understanding of data without having to pore through rows of figures.

The role of data scientists is not just analyzing data but also effectively presenting them. Hence, they would require data visualization skills to display their findings in the form of charts, graphs, line plots, time series, maps, etc.

As such, data visualization is one of the prominent data scientist skills.

There are a variety of tools for data visualization. Here are some popular ones that data scientists should know:

a) Tableau
Tableau is popular among data scientists for its ability to produce visualization output in a very short time. It enables data visualizations and graphics creation from large and constantly evolving databases.

Its efficiency and integration with advanced databases like SAP, MySQL, and Amazon AWS make it the preferred choice in companies like Nike, Amazon, Coca-Cola, etc.

b) Microsoft Power BI
Microsoft Power BI offers hundreds of tools for aggregating, analyzing, and visualizing data to make them meaningful.

It has built-in artificial intelligence and backend data integration capabilities for producing data-driven business intelligence.

The best part is that it's free and open to use, which makes it highly popular among data professionals.

c) Qlikview
A strong competitor of Tableau, Qlikview provides visualization solutions to thousands of customers across the globe.

Besides enabling customized visualization, Qlikview also includes analytics, enterprise reporting, and business intelligence capabilities. Its user-friendly interface makes it easy to create appealing, colorful visualizations.

d) D3
D3 is a Javascript library that helps produce interactive data visualization in web browsers. The flexible platform supports HTML, CSS, and SVG and enables the creation of creative graphics and pictures.

With D3, data scientists can easily map their data to the graphic attributes.

e) Others
Other visualization tools include Google Analytics, Plotly, Fusion Charts, SAS, etc.

But how do recruiters examine analytical skills in candidates? The best way is to take iMocha's analysis skill assessment test. It helps recruiters screen candidates faster, reducing the hassle involved in the recruitment process.

Find out more about the analytical skills test here.

3. Web Scraping

Web scraping is another must-have data scientist skill. It is the practice of collecting data and content available on the internet.

The data scraped could involve:

• Price comparisons
• Product reviews
• Customer sentiment
• Weather data
• Real-estate listings
• Online research
• Online presence and reputation tracking, and more.

Data scientists store these data on local files to analyze and manipulate them as needed.

The benefits of data scraping are obvious. As they say, data is the new oil, and web-scraping tools use this oil to fuel business growth.

Below are some popular web scraping tools used by data scientists:

a) BeautifulSoup
BeautifulSoup is a Python library that pulls data from webpages (HTML and XML files) and stores them locally. 

It transforms complex content into accessible Python trees for easy data navigation. It is the go-to data scraping and cleaning tool for many data scientists.

b) Scrapy
Scrapy is another Python-based scraping tool used to gather the required information from particular websites. 

Introduced back in 2008, Scrapy today is mainly used for data mining, information processing, web crawling, and extracting data through APIs.

c) Pandas
Pandas is a multi-purpose Python library that can be used alongside BeautifulSoup. The tool extracts data and exports it to excel or CSV form for easy manipulation.

The significant benefit of using Pandas is that it allows data scientists to carry out full analytics in one language.

4. Machine Learning with Artificial Intelligence (ML with AI)

All data scientists that work on complex problems involving predictions and estimations require a deep understanding of machine learning and artificial intelligence.

These tools enable producing accurate results by developing efficient algorithms and data-driven models for real-time data processing.

ML and Al tools help data professionals arrange data in logic and decision trees for easy decision-making. Companies in different sectors use these data scientist skills for the following significant purposes:

• Online fraud detection and management
• Image and speech recognition
• Traffic predictions
• Product recommendations in e-commerce businesses
• Self-driving cars
• Email, spam, and malware filtering
• Virtual personal assistance
• Predicting stock market trends
• Disease diagnosis in the healthcare industry
• Automatic language translation

5. Deep Learning with Natural Language Processing (DL with NLP)

Natural Language Processing, or NLP, is a branch of data science that focuses on training computers to interpret human languages.

As we know, the human language can be ambiguous and vary from region to region. DL, with NLP tools, look to fill the gap between data science and human languages.

Enabling machines to understand human language, apart from programming languages, improves the accuracy and efficiency of processes. Hence, data scientist learning is incomplete without the use of DL and NLP data scientist skills.

The most commonly used NLP techniques in data science are:

a) Tokenization
It is the process of dividing texts into smaller segments or tokens for more convenient analysis.

b) Stemming and Lemmatization
It is the process of reducing words to their root forms for efficient processing. For instance, 'loving' and 'lovable' stem from the word love, so they are placed in the same group.

c) Stop Words Removal
It is the process of automatically removing common or frequently used words that add little or no value to the texts.

d) Keyword Extraction
It is the process of automatically extracting the most used and most important words or expressions from a given text.

e) Sentiment Analysis
It is an NLP technique that examines the text's emotion by analyzing its tone. Businesses use sentiment analysis tools to assess customer sentiments regarding their goods, service, and brand.

6. Big Data

Most data scientists' time goes into capturing, storing, extracting, processing, and analyzing large chunks of data. Hence, it's quintessential that they should have Big Data skills.

However, handling vast amounts of data poses many physical and computational challenges. Hence, data scientists usually take the help of special tools and algorithms while working with big data.

Some of the tools required for employing Big Data data scientist skills are:

a) Hadoop
Apache Hadoop is a software framework that allows the handling of large data sets across clusters of computers. The open-source platform written in Java can store gigabytes to petabytes of data.

Many Fortune 500 companies like Amazon, IBM, Intel, Microsoft, and Facebook use Hadoop.

b) Integrate.io
As the name suggests, Integrate.io integrates and prepares data for analytics on the cloud. It's a complete toolkit for building low-code and no-code data pipelines.

Departments such as marketing, sales, and support can find immense utility for this tool for advanced analysis.

c) Cassandra
Apache Cassandra is another widely used Big Data tool for effective data management. The best feature of Cassandra is its automatic data replication functionality for high fault tolerance.

Many high-profile companies, including Accenture, American Express, General Electric, Yahoo, etc., use this open-source, cost-effective software.

d) Spark
Spark is a multi-purpose open-source framework written in Java, Scala, Python, and R. Data scientists prefer it for data analytics, machine learning algorithms, and quick cluster computing.

The popular tool is widely used by telecom and gaming companies.

e) Storm
Storm is a big data computational system offering real-time capabilities and a fault-tolerant processing system. It uses parallel calculation technology that runs across a cluster of machines.

Some prominent organizations using Storm are Groupon, Yahoo, Alibaba, and The Weather Channel.

Non-Technical Data Scientist Skills

1. Mathematics

Mathematics is integral to data science, and candidates cannot advance in this field without this critical data scientist skill.

Whether it's developing algorithms, performing analyses, or discovering insights from data, mathematics is required at every step of a data scientist's work.

Below are some key mathematical areas where the candidate needs to have a stronghold:

a) Statistics
Statistics is essential for data scientists in machine learning. Statistical models are used when classifying and organizing data sets. Some important statistical concepts for data science are:

• Central tendency, variation, covariance, correlation
• Sampling, measurement, and error
• Linear regression and regularization
• Hypothesis testing

b) Linear Algebra and Matrix
Knowing how to build linear equations is critical while developing machine learning algorithms.

From friends' suggestions on social networking sites to product recommendations on e-commerce platforms, all involve matrices and matrix algebra

c) Calculus
Calculus is used in many machine learning algorithms, and professionals must learn the skill to understand the essence of data science. It is used to find the rate of change in many small factors to optimize machine learning algorithms.

d) Probability Distribution
Data scientists use probability to denote the certainty of outcomes or the likelihood of future events. They are used as a part of the decision tree for more accurate results. 

Professionals need to be good at probability for hypothesis testing and distributions.

e) Regression
Regression analysis refers to measuring the relationship between variables. With regression, professionals can find the value of a target variable from the value of several independent variables.

In data science, regression is mainly used for forecasting events and trends or determining the strength of predictors.

f) Dimensionality Reduction
Dimensionality reduction refers to reducing the number of attributes in a data set while retaining the core information. Data science professionals need this mathematical concept to improve model accuracy by reducing input variables.

2. Domain Knowledge Skills

Many outside the data science profession often assume that data scientists just need to feed data to algorithms, and the solutions appear as if by magic. But the reality is very different.

Data scientists need to know enough about the domain they're working in to understand the business problems and goals. Without it, they may be unable to identify the correct method to apply the problem.

Domain knowledge also helps them ensure that the algorithm results make sense.

Hence, recruiters should evaluate if candidates have basic domain knowledge besides technical data scientists skills. The more they're familiar with the business and data, the better they'll be able to communicate with the stakeholders.

3. Soft Skills

As we know, data science is a very competitive field. While technical data scientist skills are necessary, they alone can't forge a strong career. Soft skills are equally essential for data scientists to stand out from the crowd and grow in their roles.

Here are some soft skills that all data professionals should develop:

a) Communication Skills

Data science is not just about programming and data analysis. It is also about communicating the insight to both technical and non-technical teams so they can use it more effective-decision making.

Hence, all data scientists must have good communication skills. They won't be valuable to the organization if they can't make a compelling case from their findings.

For this purpose, they should learn innovative communication approaches such as data storytelling. The ability to present insights as a story will bridge the communication gap from data to action.

b) Critical Thinking Skills

Critical thinking is another crucial data scientist skill. It strengthens their ability to dig deeper into the data and draw actionable insights.

Data professionals need to think rationally to understand the logical connections between data, and critical thinking helps with that.

Here are some critical thinking skills data scientists should tap into for effective problem-solving:

• Identify cognitive biases and consider alternative points of view
• Create inferences from the available information
• Perform solid research to gather accurate facts and figures
• Question everything and keep an open mind
• Analyze solutions and identify ways to improve

c) Problem-Solving Skills

Problem-solving is essential in every field, especially where data and information are involved. Being good at problem-solving starts with identifying the problem.

Real-world problems are often vague and broad. Translating them into a clear problem statement is the first step for data scientists. Once a clear problem statement is drawn, developing the most effective and efficient solution is easier.

Recruiters can test these and many other soft skills through iMocha's soft-skill assessment tests. Check out the plethora of soft skill tests available in our skill test library.

How to Assess and Hire Great Data Scientists?

By now, recruiters know the essential data scientists' skills to look for when hiring a candidate for that position. But how do they ensure that candidates actually have the skills they claim in the resume?

Validate candidates' skills by referring to our blog ‘Strategy to recruit data scientists’ and reinforce your decision.

A pre-interview skill test is the best way to measure a candidate's data scientist skills. It will help recruiters quickly determine which candidates they should go forward with.

The tests can be followed by interviews to examine the candidates' communication skills. Explore our blog for interview questions for Data Scientists to use with your candidates.

iMocha's skill test library has a set of comprehensive tests to test candidates on all essential data scientists' skills. It includes a Big Data Developer Test, Data Science Aptitude test, Data Interpretation test, Database SQL Fundamental test, D3.js test, Hadoop test, and many more.

Assess R programming and Python proficiency of your candidates by using iMocha’s skills assessments.

Could not find the data scientists' skills assessment you're looking for? Just email us at support@imocha.io. We'll help you build customized data scientists' skills tests that suit your data scientists job description.

How to Develop or Improve Essential Data Science Skills?

To make a lucrative career in data science, data scientists should continuously learn and evolve in their craft.

Improving essential skills has become increasingly important nowadays, with most employers prioritizing skill tests when hiring a data professional.

Here are some steps data scientists can take to improve their data scientists skills and crack their skill assessment test.

1. Take a Certificate Course

The key to transitioning from an entry-level data position to a six-figure data professional is mastering the essential skills through a certificate course.

A good course will familiarize aspirants with the latest tools and techniques used in data science, helping them upskill faster.

Here are some online courses to get started with for aspiring data professionals:

• Introduction to Data Science
• Data Science Specialization Course
• Python for Data Science
• Probability and Statistics for Data Science
• Introduction to SQL
• Machine Learning for Business

2. Read Relevant Materials

Another great way to improve data science skills is to read many books and academic papers related to the field. There are tons of educational materials and e-books, mostly available for free, on the internet.

It's also useful to connect with peers and seniors on LinkedIn to stay updated on the latest trends.

3. Join a Data Science Community

Connection matters in every profession, and data science is no exception. Keeping an eye out for professionals and influencers can bring numerous opportunities for networking and referrals and help with career growth.

So, data aspirants should make a point to attend data science events and get actively involved with the community

4. Participate in Open Source Projects

There is no learning better than learning from real-life experience.

Becoming a part of an open-source project of interest enables newcomers to delve deeper into data and explore and experiment with new skills. It will also add significant value to their resume and help them stay ahead of the competition.

Final Words

So these are the essential data scientist skills recruiters should look for when hiring a candidate. Which skills to prioritize will depend mainly on the business domain and requirements of the job.

For recruiters looking to hire talent faster, easier, and at scale, skill-accessing platforms like iMocha can be their perfect solution. We help organizations find excellent candidates with top data scientist skills bias-free. 

Here's how companies can benefit from skill-assessing platforms:

• Hire job-fit candidates remotely
• Scale up your hiring process
• Save time, money, and effort spent on the wrong candidates
• Provide candidates with the best recruitment and onboarding experience
• Get ready-to-use assessments or customize them as per the role requirement

Make intelligent hiring decisions with iMocha's skills analytics platform, the largest skill test library in the world. Book a demo to see how we can help you get the best-fit talent faster.

FAQs

1. How do you evaluate data scientists?
Data scientists can be evaluated on the technical and non-technical skills they possess. Below are some prominent data scientist skills to look for:

• Programming languages – Python, R, Scala, etc.
• Data analysis and visualization - Tableau, PowerBI, Qlikview, etc.
• Web scraping.
• ML with AI and DL with NLP.
• Big Data.
• Mathematics – Statistics, Linear Algebra and Matrix, Calculus, Probability Distribution, Regression, Dimensionality Reduction, etc.
• Domain knowledge skills.
• Soft skills - communication, critical thinking, and problem-solving skills.