Top data science skills to look for in candidates

1. Python

Included in 69.89% of data science job postings

Python is an object-oriented programming language, making it ideal for data analysis, machine learning and other data-science applications. Include this skill in your job descriptions if your organization relies on Python for data visualization, web development or task automation. Make your expectations clear with phrases like “in-depth understanding of Python” or “PCEP certification required.”

To verify a candidate’s Python skills, ask Python interview questions, administer a pre-employment skills test or ask for proof of industry certification.

2. Machine learning

Included in 56.83% of data science job postings

Some data scientists use machine learning to train computers to imitate human behavior. Include this skill in your job descriptions with phrases like “proficiency in statistical analysis” and “ability to perform mathematical data manipulation” if your organization uses machine learning to analyze data. 

If you need to verify someone’s machine-learning skills, administer a coding test. Another option is to ask for proof of an industry certification.

3. SQL

Included in 50.13% of data science job postings

Data scientists use SQL to extract and manipulate the data stored in structured databases. Include this skill in your job descriptions if you’re looking for a new hire who can immediately use SQL to analyze data. Use phrases like “strong proficiency in SQL” or “skill in optimizing complex SQL statements” to attract quality applicants.

If you want to verify a candidate’s SQL abilities, ask SQL interview questions, administer a pre-employment skills test or ask about their industry certifications.

4. R

Included in 45.65% of data science job postings

Data scientists use R for data analysis, data visualization and data manipulation. It’s also used to develop statistical software. Include this skill in your job descriptions if you’re looking for well-rounded applicants who can use R to your company’s advantage.

Make your expectations clear with phrases like “R proficiency required” or “ability to use R for data visualization.” As part of your hiring process, administer a coding test to test each applicant’s R skills. 

5. Data science

Included in 40.12% of data science job postings

“Data science” is a broad term, so it’s important to be specific in your job descriptions. You may need applicants who can perform advanced statistical analysis or use machine learning. If so, use phrases like “advanced statistical analysis skills required” to attract applicants.

Administer a pre-employment test to assess a data scientist’s basic skills. The test should focus on the skills your organization needs most, such as machine learning or data analysis.

6. Communication skills

Included in 37.53% of data science job postings

Effective communication is one of the most critical qualifications for data scientist job applicants. Data scientists write reports, send emails and give presentations to employees in other departments. If you include this skill in your job description, use phrases like “excellent written communication skills required.”

You can assess a candidate’s communication skills by asking scenario-based questions regarding their communication skills and methods during interviews.

7. Analysis skills

Included in 26.66% of data science job postings

Data scientists must be able to identify patterns in large volumes of data, making analysis skills essential. Include this skill in your job descriptions if you’re looking for applicants who can evaluate data effectively. Use phrases like “proficiency in data analysis required” to communicate your organization’s needs.

If you want to verify a candidate’s ability to analyze data, administer a skills test, perform in-depth reference checks or ask detailed interview questions.

8. AI

Included in 25.59% of data science job postings

Companies of all sizes need data scientists capable of working with AI technology. Include this skill in your job description if your company is already using AI or plans to use it in the near future. Be specific about your needs with phrases like “algorithm engineering skills” or “strong knowledge of AI development.”

AI is an up-and-coming field, so administering a coding test is one of the best ways to assess a candidate’s skills.  

9. Tableau

Included in 22.21% of data science job postings

Tableau is a business intelligence platform that makes it easier to understand data, so many data scientists use it daily. Include this skill in your job descriptions if you want a new hire who understands how to use Tableau for data visualization. Incorporate phrases such as “proficiency with Tableau” or “Tableau experience required.”

If you want to assess a candidate’s Tableau skills, ask Tableau interview questions or administer a data visualization test.

10. Analytics

Included in 21.02% of data science job postings

All data scientists use analytics tools in some way, making this one of the most critical data science skills. Attract quality applicants with phrases like “ability to use analytics tools to solve business problems” and “proficiency with Tableau, SAS or Power BI required.”

To assess a candidate’s ability to use analytics tools effectively, administer a skills test as part of your hiring process. Another option is to ask detailed interview questions about experience analyzing data.

11. Data mining

Included in 20.17% of data science job postings

Data mining involves extracting data and using it to identify important patterns. A data scientist must be able to use statistics, relational databases and machine learning to complete these tasks. If you decide to include this skill in your job descriptions, use phrases like “model deployment,” “data cleaning,” “data extraction” and “experience using data to make complex decisions.”

To verify a candidate’s data-mining skills, conduct a reference check or administer a skills test.

12. AWS

Included in 18.2% of data science job postings

Some data scientists work as AWS cloud engineers, so they need to understand how to use Amazon Web Services to create high-quality applications. Include this skill in your job descriptions if your organization relies on AWS in some capacity. Use AWS interview questions to tailor your requirements and add phrases like “ability to identify the appropriate cloud stack for each application.”

Assess each candidate’s AWS skills by administering a skills test or asking for proof of industry certification.

13. Natural language processing

Included in 17.35% of data science job postings

Natural language processing (NLP) combines machine learning with statistical models, giving computers the ability to understand human language. Some data scientists use NLP to analyze data and extract meaningful information. Include this skill in your job description if your organization is using NLP for data analysis or data visualization.

To assess a candidate’s NLP skills, ask them to solve a business problem using NLP.

14. Data visualization

Included in 16.36% of data science job postings

Data visualization involves using graphics to represent data. Many data scientists are responsible for creating charts, graphs and other visual representations of data, making this an important skill for your job descriptions. Attract quality applicants with phrases like “data visualization skills” or “ability to create charts and graphs to represent data.”

If you want to assess a candidate’s data visualization skills, give them a sample dataset and ask them to create a corresponding graphic.

15. Data analysis skills

Included in 15.51% of data science job postings

A data scientist must be able to extract relevant information from large datasets, so include this skill in your job descriptions if you want a new hire who can help your other employees make better decisions.

To assess a candidate’s ability to analyze data, consider administering a skills test before you make a hiring decision.

16. Spark

Included in 14.11% of data science job postings

Some data scientists use Apache Spark to process large volumes of data. Include this skill in your job description if your company uses Spark and needs a new hire who can use the system with little to no training. Attract quality applicants with phrases like “proficiency in Apache Spark” or “ability to use Spark to execute queries.”

Assess a candidate’s Spark skills by administering a skills test or asking them to describe their Spark experience in detail.

17. Power BI

Included in 13.33% of data science job postings

The Power BI platform allows users to visualize data and create custom reports, making it a valuable skill for data scientists. Include this skill in your job description if you need to hire someone with extensive experience in data visualization. Incorporate phrases like “Power BI expertise” and “ability to use Power BI to create dashboards” to attract quality applicants.

To assess Power BI skills, look for candidates with prior experience and ask to see their previous examples of dashboards or reports.

18. Azure

Included in 12.75% of data science job postings

Some companies use Azure to manage cloud resources provided by Microsoft. Include this skill in your job descriptions if you need a new hire who can use Azure to work with business data. To attract applicants who meet your hiring criteria, use phrases like “Azure expertise required” or “skill using Azure to visualize data.”

Assess each candidate’s skills by administering a skills test or requesting proof of certification, such as Azure Data Fundamentals.

19. Regression analysis

Included in 12.59% of data science job postings

Data scientists use regression analysis to assess the relationships between variables, making it easier to identify patterns and predict trends. Include this skill in your job descriptions if your company needs someone who can use regression analysis to extract meaningful information from business data.

To assess a candidate’s regression-analysis skills, ask them to complete a case study using a sample dataset. Another option is to administer a skills test to your top candidates.

20. TensorFlow

Included in 11.91% of data science job postings

TensorFlow is a machine-learning platform, making it a valuable tool for data scientists. Include this skill in your job description if your company already uses TensorFlow or plans to adopt it in the near future. Use phrases like “ability to develop NLP models with TensorFlow” or “TensorFlow experience required.”

To assess a candidate’s abilities, administer a TensorFlow skills test or ask them to explain how they’d use TensorFlow to solve a problem.

21. Pandas

Included in 11.06% of data science job postings

Some data scientists use Pandas to analyze or manipulate data. Include this skill in your job descriptions if you need to hire someone who has experience with Python programming, as Pandas is a Python-based platform. Attract quality applicants with phrases like “ability to use Pandas to analyze big data” or “experience using Pandas library required.”

To assess a candidate’s Pansas skills, ask them to complete a test exercise.

22. Java

Included in 10.95% of data science job postings

Java is an object-oriented programming language, so it has several uses in data science careers. Include this skill in your job descriptions if you’re looking for candidates who can use Java to process large datasets or train machine-learning models. Tailor the description to your exact needs by using phrases like “ability to train machine-learning models with Java.”

To assess a candidate’s Java skills, have them complete a skills test as part of the hiring process.

23. SAS

Included in 10.54% of data science job postings

SAS has several data-science applications, including data visualization, data analysis and report preparation. Include this skill in your job descriptions if you need applicants who can use SAS to perform any of these functions. Because SAS covers a broad range of functions, use specific examples like “ability to use SAS to create custom dashboards” for best results.

To assess each candidate’s SAS abilities, administer a skills test before you make a hiring decision.

24. Microsoft Excel

Included in 10.48% of data science job postings

Many companies still rely on Microsoft Excel for data cleaning, data analysis and data manipulation. Include this skill in your job description if you need to hire someone capable of using Excel for advanced functions. Set clear expectations by using phrases like “proficiency in using Excel for data analysis.” 

Assess each candidate’s skills by administering an Excel test or requesting proof of Microsoft Excel certification.

25. Hadoop

Included in 10.08% of data science job postings

Hadoop is another Apache product involved in processing large datasets. Include this skill in your job descriptions if you need to hire a Hadoop engineer who knows how to code Hadoop applications, assess a company’s big data infrastructure and troubleshoot Hadoop scripts. Use phrases like “proficiency in Hadoop application development” or “skill in using Hadoop to track data.”

To verify a candidate’s Hadoop skills, have them complete a test exercise after their initial interview.

Other top data science skills in demand

26. PyTorch: Included in 9.89% of data science job postings

27. Big data: Included in 9.01% of data science job postings

28. Git: Included in 8.85% of data science job postings

29. Computer science: Included in 8.78% of data science job postings

30. Management: Included in 8.03% of data science job postings

31. Databases: Included in 7.95% of data science job postings

32. NumPy: Included in 7.79% of data science job postings

33. Research: Included in 7.58% of data science job postings

34. Predictive analytics: Included in 7.56% of data science job postings

35. Agile: Included in 7.46% of data science job postings

36. Google Cloud Platform: Included in 7.2% of data science job postings

37. C++: Included in 7.18% of data science job postings

38. Statistical analysis: Included in 6.89% of data science job postings

39. Deep learning: Included in 6.55% of data science job postings

40. Data analytics: Included in 6.45% of data science job postings

41. ETL: Included in 6.45% of data science job postings

42. NoSQL: Included in 6% of data science job postings

43. Scala: Included in 5.94% of data science job postings

44. Project management: Included in 5.8% of data science job postings

45. Software development: Included in 5.79% of data science job postings

46. Microsoft Office: Included in 5.7% of data science job postings

47. MATLAB: Included in 5.57% of data science job postings

48. Relational databases: Included in 5.34% of data science job postings

49. Microsoft PowerPoint: Included in 5.32% of data science job postings

50. APIs: Included in 5.31% of data science job postings

¹Indeed data (US), January 2023 – Dec 2023