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Data Analyst vs. Data Scientist, What Are Their Job Descriptions?

Data Analyst vs Data Scientist Jobs Description

The field of data is growing in prominence as a key success factor for businesses today. This trend opens up new opportunities for various professionals dedicated to this field; namely Data Analyst and Data Scientist. While these titles are commonly misunderstood to mean the same, each has different roles to play and so different requirements. 

If you are thinking of recruiting someone to manage data for your company, it would be good to explore the differences between the two and make sure that you hire the right talents for your needs.

Data Analyst – Roles & Responsibilities

The main role of a data analyst is to craft meaningful stories from data accumulated by the company. Data trends identified and synthesized by the analyst would then be used by the company to make strategic decisions.

Data Analysts would need to be proficient in doing statistical analysis and relying on tools such as SQL to splice data. Furthermore, they tend to work as part of an interdisciplinary team, supporting various functions with their insights. 

In order to convey their findings to other functions clearly, Data Analysts would also need to work with visualization tools such as Tableau, Qlik and Power BI. 

What to look out for in a Data Analyst

Specific skills of an ideal Data Analyst will differ from company to company. This is because data analyst roles & responsibilities can vary widely depending on your needs. For example, Data Analysts can help you figure out why sales are dropping with regular reporting, set up dashboards in tracking KPIs and many more. However, there are still 4 core skills that every Data Analyst should have in order to be adaptable in the work environment. 

  • Structured Query Language (SQL)

This is the industry-standard database language used by all and possibly the most critical skills all Data Analysts should have. Proficiency in SQL for a Data Analyst is akin to a painter’s proficiency in using the brush. Sure there are additional skills needed to be a great Data Analyst, but all of it needs to be supported by a strong foundation in SQL.

Almost all companies will need SQL to organize their datasets. Whether you are running an Automotive Manufacturer or leading a Tech based service, placing trackers across your business operations will net you massive amounts of data. These large amounts of dataset cannot be handled by tools such as Excel, which makes Data Analysts who are proficient in SQL a key hire for you.

  • Communication Skills

Being able to manipulate data effectively to drive insights is important, but so is being able to convey these insights to the relevant stakeholders. This is necessary because, in most cases, a Data Analyst’s findings would need to be acted upon by people from other departments to make it meaningful. 

Thus, Data Analysts who are proficient in visualizing their findings (i.e. Business Analytics) and presenting it to colleagues from various backgrounds, tech or otherwise, is key for any company.

  • Critical Thinking

Before one can start splicing and dicing data to validate a hypothesis, one needs to first consider what is the hypothesis in the first place. For a Data Analyst, being able to figure out what questions to ask is just as important as how to answer them afterwards. Otherwise they may get in too deep into the data without being able to deliver quantifiable insights.

When looking to hire a Data Analyst, you need to consider their problem solving skills. During interviews, keep in mind questions like: 1. Can this individual consistently break down large problems into digestible chunks? 2. Can he/she find new perspectives when looking at situations? 3. How resourceful can this individual be?

  • Attention to Detail

When managing large datasets, there are often outlier cases that can often point to misguided insights. Data Analysts who are able to discern what is relevant from what is not will be key to guiding your company in the right direction. 

Data Scientist – Roles & Responsibilities

Data Analyst vs Data Scientist Role

If a Data Analyst’s job is to use historical data for explaining what has happened, a Data Scientist’s job would be to use it for predicting what could happen in the future. 

Data Scientists’ roles & responsibilities usually include predictive data modeling, developing data systems and automation tools. As their work often dives into areas where visibility can be limited (often due to a lack of historical data), a good business intuition is also needed to guide their actions. 

They not only need to be good at manipulating data, but also understand the business implications behind these numbers.

The main tools of a Data Scientist are SQL and Python, with which the Data Scientist can extract, debug and explore the data, and then program analysis models and predictions. This is also where the experience in machine learning processes comes in to obtain estimates on a company’s different areas of work.

Set of skills that a Data Scientist needs

The nature of a Data Scientist job is very dynamic. Which means everyday they will be needed to dive into different sets of problems facing the company and no two days are similar. Below are 4 skills we think are necessary for Data Scientist to remain adaptable and effective in their job

  • Statistical and Mathematical skills

Behind every good predictive modeling, there is a strong foundation in applied mathematics and statistics. In order to build predictions that are accurate and relevant, Data Scientists would need to know the different approaches to testing their models and select the most appropriate one. 

Expertise in the fields of calculus and linear algebra are particularly important if you rely on machine learning in your company’s forecasting. 

  • Business Acumen

There are a lot of scenarios that can be predicted. The key is to know which ones are most relevant to the business and which factors impact the most. By having a strong business acumen, Data Scientists would have the sense to prioritize their work on where it matters the most.

Furthermore, they will also be able to develop data-based solutions or automations that most effectively solve issues faced by your business. 

  • Programming Skills

A Data Scientist should be proficient in a number of programming languages. The two most common are R or Python. This is what will allow them to move beyond just predicting what could happen and develop practical solutions and applications. 

  • Communication Skills

Similar to a Data Analyst, the ability to convey one’s findings and solutions to the rest of the departments is critical to a Data Scientist’s success. After all, it does not matter how accurate or robust the predictions are if the Data Scientist is not able to deliver the action points to the executing team. 

Skill requirements of a Data Analyst vs. Data Scientist 

Despite close associations to data, both Data Analyst and Data Scientist have distinctively different roles to play. The core difference is that one uses data to look at events that have come to pass and provide an explanation for it. While the other uses data to ready the company for a potential event in the future. 

While there is certainly some overlap in terms of skills between the two, the difference is the nature of their jobs mean that each requires unique skills to be effective. Below is a list of requirements for both Data Analyst and Data Scientist. 

RequirementsData AnalystData Scientist
Education– Bachelors in Mathematics, Computer Science, Computer Engineering– Bachelors in Mathematics, Statistics, Computer Science, Computer Engineering
– Advance degrees in related field would be desirable
Programming Language & Tools– SQL- Excel (Macros)- Business Intelligence (e.g. Tableau, PowerBI)– SQL- Business Intelligence (e.g. Tableau, PowerBI)- Python- R


As businesses are acknowledging the importance of data-driven decision making, this has driven up the demand for both positions. Hiring either one will definitely cost you quite a sum, but the benefits they will bring remains unquestionable. 

Therefore, it would be prudent to understand the distinction between the two and identify which one fits your company’s requirement the best.

Do you want to find a suitable Data Analyst or Data Scientist candidate for your company? Use Shortlyst’s powerful sourcing tools for recruiters to find them now. Start for free now!

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Shortlyst Team

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