Key Takeaways
- A U.S. data analyst typically earns between $70K and $100K/year; data scientists earn higher, usually $110K+.
- Tools and outputs differ: analysts deliver dashboards and reports, while scientists drive predictive modeling, automation, and experimentation.
- Early-stage teams benefit most from hiring an analyst first; add a scientist later as your data infrastructure matures.
- Offshore hiring, especially throughTalent Hackers can significantly reduce costs while maintaining ownership, alignment, and scale.
- Build your analytics stack strategically: start with an offshore analyst for immediate impact; add a scientist later for technical depth.
- Not sure which role fits your needs? Check out our guide on how to scope data roles and if you’re still on the fence, ask us.
Every founder hits a point where they know their business needs better data, but the roles sound confusing. Data analyst or Data scientist. Both are in-demand and command serious salaries while both claim to be the key to unlocking growth.
Here’s the problem: they’re not interchangeable. A data analyst turns numbers into dashboards and reports that help you make decisions today. A data scientist builds predictive models and experiments that shape what your business can do tomorrow.
Hire the wrong one at the wrong stage, and you’ll either overspend on skills you don’t need yet — or underinvest and miss opportunities to grow.
In this guide, we’ll break down the differences in skills, tools, outputs, and costs between data analysts and data scientists. You’ll also see when each role makes sense, how salaries compare in the U.S. vs offshore markets, and how to structure your first data hire in 2025.
By the end, you’ll know which role your business actually needs right now and how to hire them without breaking your budget.
Here’s a complete guide of all you need to know about hiring a data analyst.
What Does a Data Analyst Do?
A data analyst is your bridge between raw numbers and real business decisions. Their main job is to take the messy data your business produces every day and translate it into reports, dashboards, and insights you can actually use.
Responsibilities typically include:
- Cleaning and structuring data from different sources
- Running queries with SQL or Excel to answer specific business questions
Building dashboards in tools like Tableau, Power BI, or Looker - Tracking KPIs to show what’s working and what isn’t
- Helping leaders make informed decisions with clear, visual reporting
There are different types of analysts depending on your business:
- Business Analyst: focuses on processes and operations
- Product Analyst: tracks user behavior, funnels, and experiments
- Marketing Analyst: measures campaigns, ROI, and audience insights
- Operations Analyst: improves efficiency in logistics, supply chain, or finance
Junior analysts tend to execute more (running reports on request), while senior analysts are proactive problem-solvers who spot trends before you ask. If you want a dig deeper into the different types of analysts and how they fit into a team, check out our guide on Types of Data Analysts.
What Does a Data Scientist Do?
A data scientist takes data a step further. While analysts focus on describing what’s already happened, scientists build models that predict what’s likely to happen next and design experiments to test it.
Responsibilities typically include:
- Designing predictive models and algorithms
- Running advanced statistical analyses and machine learning workflows
- Using Python, R, or TensorFlow for deeper modeling
- Building recommendation systems, forecasting tools, or automation pipelines
- Partnering with product and engineering teams to experiment and optimize
The role is more technical and innovation-focused than an analyst’s. Where an analyst explains “what the numbers say,” a scientist helps your business answer “what should we do next?”
Common specializations include:
- Machine Learning Scientist: builds models for predictions and personalization
- Research Scientist: experiments with algorithms and new techniques
- Applied Data Scientist: bridges business problems with real-world solutions
At junior levels, data scientists often build and test models under guidance. At senior levels, they become strategic leaders who set data direction, guide experimentation, and help shape the company’s future products. For businesses still figuring out how to scope data hires, it’s worth pairing this with a clear understanding of how to hire offshore data analysts since analysts are often the first building block before you add scientists.
Data Analyst vs Data Scientist: Skills, Tools, and Outputs Head-Head Comparison
It’s easy to mix up analysts and scientists because both work with data. But the scope, skill sets, and outcomes they deliver are very different. Let’s look at them;
Most startups start with a data analyst to get clarity on what’s happening inside the business. A data scientist usually comes later, once you’ve built a baseline and are ready for predictive modeling or experimentation. If you’re weighing costs, our Data Analyst Salary Guide gives a detailed look at how rates shift across roles, regions, and levels of experience.
When Should You Hire a Data Analyst vs a Data Scientist?
Hiring the right role isn’t just about skills but timing too. Each role adds the most value at different stages of your business.
A Data Analyst makes sense if you:
- Are an early-stage or growth-stage startups
- Need clarity on customer behavior, revenue trends, or campaign performance
- Are looking for reporting, dashboards, and insight-driven decision support
- Want to optimize operations and spot inefficiencies quickly
A Data Scientist makes sense when:
- Scaling-stage or product-mature companies
- Need predictive modeling, personalization, or recommendation engines
- Running A/B tests and experiments to guide product or pricing decisions
- Looking to automate data pipelines or build ML-driven features
Most startups benefit from hiring a data analyst first to get the fundamentals right. Once you have reliable reporting and KPIs in place, adding a scientist helps you move into predictive and experimental territory.
Founders who are still deciding between the two should also think about scope and ROI. Our guide on how to hire offshore data analysts covers the onboarding, ramp-up, and cost advantages of analysts and insights that can help frame when to bring in a scientist later.
Why Offshore Hiring Works
The difference between a good hire and a great one isn’t just skills. It’s ownership.
A data analyst who simply runs reports when asked will always be reactive. But an analyst who thinks like an owner will flag trends you didn’t see coming, suggest experiments, and connect data back to business outcomes. The same is true for data scientists, some build models in isolation, while others take initiative to shape product direction and drive growth.
This is where offshore hiring works. At Talent Hackers, we don’t just source based on technical ability. We vet for ownership mindset, professionals who step into your business as if it’s their own. That means you’re not just getting a SQL wizard or a Python pro; you’re getting someone who asks the right questions and makes decisions with your company’s success in mind.
For a closer look at how offshore teams bring both cost savings and higher impact, see our guide on the best countries to hire offshore data analytics talent
Which Role Should You Hire in 2025?
If you’re building your first data function, start with a data analyst. They’ll give you the reporting, dashboards, and insights to make smarter day-to-day decisions.
Once your company matures, with enough data volume to run experiments or power predictive models , then it’s time to layer in a data scientist. That’s when algorithms, personalization, and advanced forecasting begin to pay off.
The salary gap is real: U.S.-based analysts average $70K–$100K, while scientists push $110K+. Offshore, you can often hire both at 70–80% less without sacrificing skill or ownership.
Most founders should hire an offshore analyst first, then add a scientist as data maturity and product complexity grow. That sequencing saves money, reduces hiring friction, and gets your company the right insights at the right time.
If you’re not sure which role fits your business stage right now, we can help you scope the role, budget, and talent you actually need.
FAQ: Data Analyst vs Data Scientist
1. What’s the main difference between a data analyst and a data scientist?
A data analyst focuses on reporting and dashboards, turning raw data into insights you can use today. A data scientist builds models and experiments that predict future outcomes and help optimize products at scale.
2. Which role should a startup hire first?
Most startups benefit from hiring a data analyst first. They provide the immediate clarity needed for decision-making. A data scientist usually comes later, once you have enough data volume and infrastructure to run predictive models and experiments.
3. How much does it cost to hire each role?
In the U.S., data analysts average $70K–$100K per year, while data scientists often earn $110K+. Offshore, the cost drops significantly with analysts averaging $9K–$20K and scientists $18K–$35K in markets like Nigeria and Kenya. For more detail, see our Data Analyst Salary Guide.
4. Are offshore data analysts and scientists reliable?
Yes, especially when sourced through vetted platforms. At Talent Hackers, we focus on candidates with an ownership mindset, not just technical skills. That means offshore hires plug into your team as if they were in-house.
5. What tools do analysts and scientists use?
Analysts rely on SQL, Excel, and BI tools like Tableau or Power BI. Data scientists work with Python, R, and ML frameworks such as TensorFlow or PyTorch.
