.webp)
Key Takeaways
- Understanding whether you need a data analyst or data scientist comes down to what you want to do with your data, but many businesses benefit from having both.
- A data analyst focuses on structured reporting and trend analysis, while a data scientist builds AI models and predictive algorithms.
- Common misconceptions about data analysts and data scientists include that you can hire one instead of the other and that they do the same job.
If data is the new gold, then data analysts and data scientists are the modern-day prospectors. Yet, both dig for insights using different tools. With companies relying on data more than ever, these roles are in high demand, and their average salaries reflect that. However, hiring the wrong expert can leave businesses drowning in numbers instead of making sense of them.
So, when it comes to data analysts versus data scientists, which one does your business actually need? Choosing the right one can mean the difference between informed decisions and wasted resources.
This guide looks at their key differences as well as when to hire each and how much it costs to hire.
Understanding the Roles: Data Analyst vs. Data Scientist
At first glance, data analysts and data scientists might seem like two sides of the same coin. They both work with data, provide valuable insights that help businesses make decisions, and are in high demand. But in reality, their roles, skills, and impact are very different.
Data analyst jobs account for about half of all data-related job postings, while data scientist roles make up only around 15%. These statistics suggest that most companies need data analysts for everyday business analytics and intelligence, while a smaller but still significant number invests in data scientists for more advanced analytics and machine learning applications.
So, what exactly sets them apart?
What does a data analyst do?
A data analyst is an expert in interpreting structured data such as sales numbers, customer trends, and operational metrics. Their crucial role involves the following:
- Cleaning and organizing data to make sure it’s accurate.
- Using analysis tools like SQL, Excel, and BI tools (e.g., Tableau and Power BI) to create reports and dashboards.
- Identifying key trends and patterns to support business decisions.
There are different types of data analysts, including:
- Business analysts: Focus on financial and operational data to yield actionable insights that can guide business strategy.
- Marketing analysts: Specialize in customer behavior, campaign performance, and market trends.
- Financial analysts: Analyze revenue, expenses, and investment opportunities.
- Healthcare analysts: Work with patient data, medical records, and hospital performance metrics.
- Operations analysts: Optimize supply chain efficiency, logistics, and internal workflows.
Data analysts focus on making data digestible and presenting their findings to stakeholders who need insights to act on.
What does a data scientist do?
A data scientist takes things a step further by applying statistical modeling, machine learning, and artificial intelligence (AI) to predict future outcomes. Their work includes the following:
- Building predictive models that forecast trends.
- Developing machine learning algorithms to automate decision-making.
- Using programming languages like Python and R for complex analysis.
The data scientist’s role is less about organizing data and more about extracting deeper insights and automating processes.
The demand for data scientists is expected to grow in industries requiring heavy R&D and AI innovation. Currently, 49% of job openings for data scientists come from IT and tech companies, and 69% of job postings for data scientists request machine learning skills—a major differentiator from data analysts.
Key differences: Data analyst vs. data scientist
Here, we’ve created a simple table to highlight the distinct differences between data analysts and scientists:
%20(10).webp)
So, the decision to hire a data analyst versus a data scientist depends on what your business needs: structured insights for decision-making or complex models to automate and predict future trends.
%20(1).webp)
When Should You Hire a Data Analyst vs. a Data Scientist?
Hiring the right data expert comes down to one key question: What do you need your data to do?
If you’re looking for clear reports and visualizations, a data analyst is the way to go. But if you need predictive models and automation, a data scientist is the better fit.
Hire a data analyst if:
- You need structured reports and dashboards for business decision-making. Data analysts are experts in SQL, Excel, and BI tools like Tableau and Power BI, transforming raw numbers into easy-to-read insights for business leaders.
- You want customer behavior analyses, sales tracking, or financial forecasting. Whether it's monitoring trends, optimizing pricing, or measuring marketing performance, analysts help businesses understand what’s happening in real time.
Hire a data scientist if:
- You need AI-driven insights or machine learning models to predict future trends. Data scientists analyze data, but they also build predictive algorithms that anticipate customer behavior, market fluctuations, and operational risks.
- Your company deals with unstructured data (e.g., social media sentiment, image recognition). Unlike analysts, who focus on structured data, data scientists work with text, images, and even audio to extract deeper business insights.
- You require complex automation and data engineering capabilities. Data scientists use advanced tools like Python, R, and TensorFlow to automate processes, detect anomalies, and optimize decision-making at scale.
Common Misconceptions About Data Analysts and Data Scientists
There’s a lot of confusion about data analysts and data scientists, and hiring managers often misunderstand what each role actually does.
These misconceptions can lead to bad hiring decisions, wasted resources, and misaligned expectations. Below, we clear up some of the biggest myths.
1. “They do the same job.”
Not even close. Data analysts focus on past and present trends; they help businesses understand what happened and why it happened using structured reports and dashboards.
Data scientists, on the other hand, use machine learning and AI to build predictive models that predict what will likely happen next.
2. “A data scientist can replace a data analyst.”
While both roles work with data, they solve completely different problems. A scientist might build an algorithm to predict customer churn, but an analyst would track retention metrics and spot current trends.
Most companies actually need both roles working together.
3. “Data analysts don’t need to code.”
While analysts may not be building AI models, coding skills are still essential. Many use SQL, Python, and R to clean data, automate reporting, and analyze large datasets.
Without coding, their ability to extract insights would be limited.
4. “Data scientists work alone on AI models.”
Data science isn’t a solo act. Scientists collaborate with AI developers, analysts, data engineers, and business teams to align models with business goals. A machine learning algorithm is useless if it’s not grounded in the needs of real-world business operations.
5. “Hiring a data scientist means you don’t need an analyst.”
Companies that rely heavily on data almost always have both. Analysts handle daily reporting and insights, while scientists build long-term predictive analytics tools.
They serve different functions but work best together.
How Much Does It Cost to Hire a Data Analyst or Data Scientist?
Hiring a data expert with specialized skills isn’t cheap, especially in the US. In fact, data scientists have now joined the exclusive club of six-figure earners, making employing them a significant investment for businesses.
Meanwhile, data analysts, while more affordable, still command high salaries due to the growing demand for data-driven decision-making.
Although both analysts and scientists can work remotely, recent survey results have indicated only 5% of companies explicitly list these job roles as remote in job descriptions.
As a result, many businesses are limiting their talent pool by focusing only on local hires. This is despite the fact that equally skilled professionals are available at significantly lower costs in regions like Latin America (LatAm).
US vs. LatAm annual salary comparison
With potential savings of up to 70%, hiring analysts and scientists from LatAm is a cost-effective way to build a skilled data team without sacrificing expertise.
According to our guide to data role salaries, businesses can expect the following savings when hiring data specialist talent in LatAm:
%20(11).webp)
The cost advantage of hiring in LatAm
Key benefits of hiring LatAm-based data experts:
- They work in the same or similar time zones as the US, making collaboration seamless.
- Highly skilled professionals with salary expectations significantly lower than their US-based peers due to lower living costs.
For companies looking to cut costs without cutting talent, hiring a LatAm data expert through a recruitment agency is a smart move.
Final Thoughts
Choosing between a data analyst and a data scientist ultimately comes down to what you need from your data.
- Need reporting, dashboards, and trend analysis? Hire a data analyst.
- Need predictive models, AI-driven insights, or automation? Hire a data scientist.
Realistically, though, you probably need both, which can be prohibitively expensive, especially for new businesses.
The good news is that finding the right expert to fill your data scientist or analyst roles doesn’t have to be expensive. Remote hiring in LatAm gives you access to top-tier talent at a fraction of US costs without compromising on skills or experience.
Near can help with this. We connect businesses with highly skilled LatAm talent, including data analysts and data scientists, through a streamlined, personalized hiring process.
Book a free consultation call today to discuss how we can help you get the data talent you need for 30–70% below US market rates.
.webp)
.webp)
%20(2).webp)
.webp)
.webp)
.png)
%20(1).png)
%20(1).png)