Data scientists are integral to an organisation's operational decision-making process. They deal with large sets of data, analysing and translating the data into an easy-to-understand format that an organisation can use to inform its next steps.
Data scientists can work across many different sectors or businesses, and use various scientific principles or concepts in their analysis of data before interpreting their results and reporting on their findings. The work of a data scientist enables those in the business to make informed decisions based on statistical evidence.
This article outlines the skills and abilities that successful data scientists demonstrate. We also explore how you can best test these skills to shortlist suitable candidates in your data scientist applicant pool.
What should a data scientist be able to do?
Many employers employ data scientists across both the private and public sectors. No matter which industry they work in, there are many common tasks that data scientists are expected to perform as part of their job.
- Using relevant statistical software and techniques to analyse and interpret large data sets.
- Cleaning up and manipulating large data sets into workable data using various statistical software packages
- Applying relevant sampling techniques to understand and make inferences from the data sets
- Creating and using statistical modelling to interpret data
- Performing relevant statistical analysis on data to identify trends
- Testing and validating statistical models and hypotheses
- Creating appropriate forms of data visualisations such as graphs, infographics, or charts to represent data and findings from the analysis
- Writing reports to explain findings from statistical analysis and modelling
- Proposing recommendations or solutions based on findings from statistical analysis
- Identifying any trends or patterns in the data and reporting on these findings
- Designing appropriate instruments to collect further data, such as polls or surveys
Skills to look for in a data scientist
The role of a data scientist is a highly skilled one. To be successful, you need to demonstrate a high level of proficiency across many areas.
Analytical skills: data scientists need to use their analytical skills daily when dealing with data sets. Using analytical thinking to determine which statistical models or packages to use, what sampling techniques to perform, and how to tackle the evaluation of data. Strong analytical skills are essential for all data scientists, no matter what sector or industry they work in.
Critical thinking skills: alongside strong analytical skills, data scientists need to demonstrate critical thinking when dealing with, manipulating, and evaluating data. Displaying intellectual curiosity in questioning your methods of analysing data, techniques used, and findings gained ensures that data is evaluated appropriately, helping to validate the findings and conclusions.
Statistical modeling skills: data scientists need to use statistical modeling when dealing with large data sets. Having the skill and understanding to determine which statistical models to use on what data sets are crucial.
Programming skills: all data scientists need to use software and packages to analyse data, create statistical models and use these to evaluate the data. Therefore, demonstrating proficiency in programming skills is essential to the success of the analysis of data and a necessary skill requirement for all data scientists.
Data mining skills: being able to extract data from large amounts of data is an important skill required of all data scientists. Understanding what software packages to use when data mining and using this skill effectively across sizeable raw data sets provides the building blocks of effective data analysis. Meaning data can be used to make informed predictions.
Communication skills: to be an influential data scientist, you need to be able to document and present your findings to those that may not have a background in data science. Demonstrating excellent written and verbal communication skills is an essential skill that all data scientists need to have for their work to be used effectively and for the organisation's benefit.
Useful abilities for a data scientist
Data scientists need to demonstrate several abilities to carry out their roles successfully, such as:
Attention to detail: having attention to detail when working with and sampling large data sets is critical to successfully analysing and evaluating data. When dealing with data, a lack of attention to detail could lead to incorrect assumptions or findings, resulting in the wrong decision, which can have severe consequences for the organisation.
Clarity of communication: while data scientists need to demonstrate a high level of technical expertise, they also need to be able to translate their findings into an easy-to-understand format. The ability to explain their findings to someone who doesn't have the same level of technical understanding is fundamental to the success of a data scientist.
Problem-solving: integral to the role of a data scientist is the ability to solve problems. Thinking laterally and critically when evaluating data, findings, and recommendations means that data scientists can ensure that the data has been analysed correctly and confirms the validity of their findings and recommendations.
Teamwork: while data scientists spend a lot of their time working independently, they also need to demonstrate the ability to work in a team. Working with others on large data sets, liaising with other departments on modelling, sample collection, and communicating their findings means teamwork is an essential part of their role.
What soft-skills tests could I use to hire a data scientist?
Ensuring you hire the right people for your data scientist role starts with evaluating both the soft skills and technical skills and abilities of those in your applicant pool. Assessing soft skills can be tricky when all candidates state the same experience level.
Using the relevant soft skills tests provides an objective way to narrow down your candidate pool to only those that demonstrate the soft skills you require for your data scientist roles.
Some soft skills tests that you can use are:
Teamwork test: this multiple-choice test assesses whether candidates can work together with others effectively and productively. The test uses hypothetical situations to measure a candidate's collaboration abilities and ability to work in a team.
Interpersonal skills test: an interpersonal skills test provides an opportunity to assess candidates on how well they interact and communicate with those around them. The test evaluates a candidate's emotional intelligence when dealing with challenging situations such as conflict resolution and their empathy and relationship-building skills.
Accountability test: showing integrity when dealing with data and discipline in your role are essential skills for a data scientist. The accountability test assesses candidates against their beliefs, skills, and attributes to identify those candidates who are a good match for the organisation's culture and ethos.
Which technical or aptitude tests could I use to hire a data scientist?
The role of a data scientist requires a high level of technical expertise and skills. To objectively assess the level of skill applicants have, it is advisable to include relevant technical skills and aptitude tests in the recruitment process. Those candidates who score highly on these tests are likely to have the skill level required to carry out a data scientist's role effectively.
Recommendations on technical skills and aptitude tests that can use to test candidates include:
Software skills: to effectively carry out their job, data scientists need to be proficient in using different types of software. Incorporating relevant software skills tests such as an Excel skills test into your recruitment process means you can assess a candidate's skill level pertinent to the software requirements of your data scientist role.
Programming skills: data analysis often requires data scientists to draw on their programming skills to create and use statistical models. Including a programming skills test ensures that the candidates you shortlist demonstrate proficiency in a given programming language.
Abstract reasoning: reasoning and solving problems using abstract thinking are essential for data scientists. Including an abstract reasoning test enables you to determine whether candidates demonstrate the ability to use logic to solve problems and how well they can cope with problem-solving using large sets of data.
Error checking: data scientists deal with large amounts of data in their role. The ability to spot anomalies in data sets is fundamental to the success of their subsequent analysis of data. Using an error-checking test as part of your recruitment process enables you to determine objectively whether candidates demonstrate this essential skill.
Logical reasoning: solving complex problems using data requires the ability to think and reason logically. A logical reasoning test evaluates this ability and whether candidates can problem solve by making sense of information to identify patterns, relationships, or trends within data.
Our recommended test battery for a data scientist
Several test batteries are available to help you identify only those candidates that demonstrate the skills and abilities needed to be a successful and effective data scientist.
Our recommendations include:
Interpersonal skills test: to ensure the individual you hire can effectively communicate their findings and recommendations with others both verbally and in writing
Accountability test: ensuring that whoever you bring into your organisation as a data scientist aligns with the values and purpose of the organisation and the role and can act with integrity when working with and analysing the data in making appropriate recommendations.
Excel test: ensuring that your preferred hire is able to use and work with both the basic and advanced features of Microsoft Excel to work with, manipulate, analyse and interpret statistical data sets.
For more information about hiring a data scientist, check out our page on data scientist tests.