Data Scientist vs Machine Learning Engineer.
Same industry, different day-to-day. Here is how the two roles actually differ — skill by skill, from Skilture's profile of each role.
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Data Scientist
Analyzes complex datasets to extract insights, build predictive models, and drive data-informed decision making. Combines statistical analysis, machine learning, and domain expertise to solve business problems.
29 tracked skills · 7 core
Full Data Scientist skill breakdownMachine Learning Engineer
Designs, builds, and deploys machine learning models and systems at scale. Bridges the gap between data science and software engineering to productionize AI/ML solutions.
28 tracked skills · 7 core
Full Machine Learning Engineer skill breakdownSalary snapshot
Data Scientist
$120,230/yr median
$67,240 – $199,130 (10th–90th percentile)
Source: O*NET OnLine (BLS Occupational Employment and Wage Statistics) (SOC 15-2051.00, 2025)
Machine Learning Engineer
$135,980/yr median
$82,460 – $214,670 (10th–90th percentile)
Source: O*NET OnLine (BLS Occupational Employment and Wage Statistics) (SOC 15-1252.00, 2025)
US figures in US dollars (BLS/O*NET); we have no sourced Indian figure yet.
14 skills both roles expect
These transfer directly if you switch between the two paths — but notice where the importance differs. Tap any skill to see why it matters.
| Skill | For Data Scientists | For Machine Learning Engineers |
|---|---|---|
| Python | Core | Core |
| Machine Learning | Core | Core |
| SQL | Core | Important |
| Pandas | Core | Important |
| TensorFlow | Important | Core |
| PyTorch | Important | Core |
| AWS | Important | Core |
| Scikit-learn | Important | Important |
| NLP | Important | Important |
| GCP | Nice-to-have | Important |
| Azure | Nice-to-have | Important |
| Apache Spark | Nice-to-have | Important |
| MLOps | Nice-to-have | Important |
| Collaborationsoft | Important | Nice-to-have |
Where the paths diverge
The skills each role expects that the other doesn't — this is the real cost of choosing one path over the other.
Only Data Scientists need
15 skills, 3 of them core
Only Machine Learning Engineers need
14 skills, 2 of them core
Still torn? Let your actual skills decide.
Upload your resume and score yourself against both roles. See which one you're already closer to — and exactly what it takes to close the other gap.
Run the full analysis (free)Data Scientist vs Machine Learning Engineer: common questions
- What is the difference between a Data Scientist and a Machine Learning Engineer?
- Data Scientist: Analyzes complex datasets to extract insights, build predictive models, and drive data-informed decision making. Combines statistical analysis, machine learning, and domain expertise to solve business problems. Machine Learning Engineer: Designs, builds, and deploys machine learning models and systems at scale. Bridges the gap between data science and software engineering to productionize AI/ML solutions. The two roles share 14 skills, while 15 skills are specific to Data Scientists and 14 to Machine Learning Engineers.
- Which skills do Data Scientists and Machine Learning Engineers share?
- Skills that are core to both roles include Python, Machine Learning. In total the roles overlap on 14 skills, which makes it realistic to move between them.
- How do I know which role my current skills are closer to?
- Upload your resume at Skilture and check your Resume Score against each role. In about 60 seconds per role you see which one you're already closer to and exactly which skills you'd need to close the gap — free, no signup required.