As we move through 2025, starting salaries in data science are reaching new highs. But is $150,000 realistic or just internet noise?
Whether you’re changing careers, finishing a degree, or wrapping up a bootcamp, you’re not just chasing a job…you want a strong return on your time and effort. This guide breaks it all down.
We’ll show you who’s getting hired, what entry-level pay looks like right now, the difference between base salary and total compensation, and how to push your offer higher.
The truth? Candidates with practical, job-ready skills are the ones landing six-figure offers, and often more.
“Entry-level” gets thrown around a lot, but in data science, it doesn’t only mean fresh-out-of-school. If you’re landing your first formal role in the field, you’re considered entry-level, no matter how you got there.
You likely fall into one of these buckets:
You’re not new to working…you’ve just been working in a different field. Maybe you spent five years in finance or managed marketing campaigns or even worked as a biologist.
Now, you’ve added Python, SQL, and machine learning to your toolkit and you’re making a shift. The good news? Your domain experience is gold. Companies value people who can translate numbers into real-world decisions.
You’ve got the degree…maybe in statistics, econ, or computer science, but you haven’t had much chance to apply your skills on the job. This is where a solid project portfolio makes all the difference. Employers want to see that you can actually do data science, not just talk about it.
You just wrapped up a research-heavy doctorate. You know how to code, model data, and write technical papers.
But now, you need to apply those skills to business problems like predicting churn or improving logistics. With the right framing, your academic background can be a serious edge.
Salaries in 2025 aren’t just rising…they’re splintering. Two people with “entry-level” titles could be earning $40,000 apart depending on where they work and what they bring to the table.
Entry-level data science roles are commanding impressive pay right out of the gate. Even first-year hires are seeing six-figure offers.
What you earn on paper isn’t always the full picture. Total compensation includes bonuses and stock options that can push your earnings much higher.
Not every data job pays the same. Your title and what you actually do day to day makes a big difference.
| Role Title | Starting Base (U.S., 2025) | Key Skills That Matter |
| Data Analyst | $75,000 to $100,000 | SQL, Tableau, Excel, Communication |
| Data Scientist | $117,000 to $165,000 | Python, SQL, ML, Statistics, Cloud Basics |
| Machine Learning Engineer | $125,000 to $175,000+ | Python, TensorFlow/PyTorch, MLOps, AWS/GCP |
Data analysts tend to support decisions and reporting. Data scientists build models and run experiments. ML engineers take those models and scale them. Pick the track that aligns with what you actually enjoy doing.
The top of the salary range comes from a handful of big names. Getting hired at one of these places takes more than just technical skills…it takes preparation, polish, and a confident portfolio.
| Company | Typical Base Salary | Estimated Total Comp (TC) | Notes |
| Microsoft | $118,000 to $145,000 | $140,000 to $170,000+ | Consistent bonuses and strong internal mobility |
| $140,000 to $170,000 | $170,000 to $210,000+ | Intense interviews, big rewards | |
| Finance/Hedge Funds | $150,000 to $200,000 | $200,000 to $250,000+ | Niche skills in statistics, trading models, risk analysis often required |
Keep in mind: these companies have high bars, but they also offer programs and internships designed to grow entry-level talent. If you’re aiming high, aim early.
Your pay isn’t just about where you apply. It’s about what you bring with you.
Yes, remote work is real…but salary bands are still higher in:
Even if you’re remote, many companies anchor offers based on HQ location or regional cost-of-living bands.
Some tools just command better offers. If you know these, you’re more likely to land at the higher end:
If you’re applying as a data scientist and know production pipelines, that’s a $20,000+ difference right there.
You don’t need to work at a tech giant to make good money. Some of the best pay in 2025 comes from:
These sectors use data to stay profitable, fast, and scalable. That means they’re willing to pay more for folks who can find patterns, reduce risk, or increase efficiency.
Being able to explain your model’s results to a non-technical stakeholder can add $10k to $15k during negotiations. Learn to tell a story with data. Better yet, rehearse it until it sounds natural.
Not everyone hits $150k out of the gate. But if you follow this roadmap, your odds go way up.
Internships are the gold standard, but not everyone gets one. If you’re in a bootcamp, your portfolio is your experience. Treat it seriously.
Make your GitHub public. Document your projects like you’d explain them to a hiring manager and include business context: why does your project matter?
Instead of being okay at everything, be excellent at one thing. Employers remember candidates who show focused, repeatable success. Consider specializing in:
One high-impact project in one of these areas beats five half-finished notebooks any day.
Practice the stuff they’ll actually ask:
Mock interviews help, and so do peer review groups. Don’t just memorize answers…practice sounding like someone who already works there.
You don’t need to be pushy…you need to be prepared.
That $5,000 to $15,000 bump? It’s often there for the asking.
Stock options. 401(k) match. Education stipends. Annual bonuses. Paid relocation.
Sometimes, a “lower” offer can be worth much more when you factor in the full package. Do the math.
In 2025, entry-level data science roles typically pay between $117,000 and $170,000. Where you land on that range depends on what you know, how well you show it, and how you position yourself during the hiring process.
If you’re serious about hitting the upper end, think of a bootcamp or focused training as more than just education…it’s an investment in your future salary. The demand is real, the money is there, and with the right preparation, you can absolutely earn it.
