Compare · Updated 16 September 2026
Data Scientist vs Machine Learning Engineer: which pays more and which is faster?
Same sourced data as the career pages, side by side.
Machine Learning Engineer pays more at mid-career: a median of $200,000 against $140,000 for Data Scientist, about 43% higher. Data Scientist is faster to enter: the quickest verified route takes about 12 months versus 18 for Machine Learning Engineer. Job growth favours Data Scientist (35% projected over ten years, BLS 2025-35, versus 10%).
| Data Scientist | Machine Learning Engineer | |
|---|---|---|
| Entry median | $110,000 | $140,000 |
| Mid-career median | $140,000 | $200,000 |
| Senior median | $180,000 | $270,000 |
| Top end | $199,130 | $497,500 |
| Roadmap hours | 890 | 1,360 |
| Fastest way in | Domain expert converting (12 mo) | Data engineer to ML engineer (18 mo) |
| Cheapest way in | $0 | $800 |
| Time to first job | 18–36 months | 36–60 months |
| Degree | No degree is legally required, but a quantitative master's or PhD is a hard filter in pharma, biostatistics, clinical research and quantitative finance; product analytics teams at technology companies and most mid-size employers are where demonstrated skill substitutes for the credential. | No degree is legally required, but Machine Learning Engineer is the least degree-flexible of the data roles in practice: most large-employer postings list a bachelor’s degree in computer science or another quantitative field, and a master’s is still the standard credential for new entrants. A portfolio substitutes for it only when it contains deployed systems or open-source contributions, not notebooks. |
| 10-year growth | 35% | 10% |
| Openings per year | 24,800 | 106,100 |
| Automation exposure | medium | low |
| Key certification | IBM Data Science Professional Certificate | AWS Certified Machine Learning Engineer - Associate (MLA-C01, succeeded by MLA-C02) |
| Tools | Python, SQL (Snowflake, BigQuery, Databricks, Redshift), Jupyter / VS Code, scikit-learn, XGBoost / LightGBM | Python, PyTorch, scikit-learn, XGBoost / LightGBM, Spark |
Salary figures checked September 2026 (Data Scientist) and September 2026 (Machine Learning Engineer). Sources are listed on each career page.
What a Data Scientist does
A Data Scientist is a specialist who estimates what would happen rather than reporting what already did, by designing and reading out A/B tests, building forecasting and propensity models, and using causal inference when a clean experiment is impossible.
A data scientist sits between the analyst and the engineer. Where an analyst reports what happened, a data scientist estimates what would happen: designs and reads out A/B tests, builds forecasting and propensity models, does causal inference when a clean experiment is impossible, and builds the metric frameworks the rest of the company argues over. In practice the job splits into two archetypes. Product / decision science - heavy on SQL, experimentation, causal methods and stakeholder influence, common at consumer tech, marketplaces and fintech. Modelling / applied science - heavy on Python, scikit-learn, feature engineering and model evaluation, common in insurance, credit risk, healthcare, pricing and demand forecasting. Read the job description carefully, because the interview loops for the two are almost entirely different.
- BLS median of $120,230 with 35% projected growth to 2035 - one of the strongest wage-and-growth combinations in the US labour market.
- Genuine intellectual variety: the same week can involve causal inference, forecasting and a metric argument.
- Portable across industries; the statistics travels even when the domain does not.
What a Machine Learning Engineer does
A Machine Learning Engineer is a software engineer who owns machine learning models in production: the pipeline that assembles training data, the evaluation that decides whether a new model ships, the serving path that answers in milliseconds under load, and the monitoring that catches drift.
A machine learning engineer owns models in production. That means the pipeline that assembles training data, the training job itself, the evaluation harness that decides whether a new model is better than the old one, the serving path that answers in 50 milliseconds under load, and the monitoring that catches drift before a stakeholder does. At large companies you will specialise - ranking and recommendations, ads, search relevance, fraud and risk, speech or vision, or the platform team that builds the training infrastructure everyone else uses. At small companies you are the entire ML function, which means you also do the data engineering.
- Among the highest-paid individual contributor roles outside of finance: Levels.fyi US median total compensation of $280,000 and 90th percentile of $497,500.
- Work is intellectually dense and the feedback is objective - the model is better or it is not.
- Skills transfer cleanly to AI engineering, data engineering, backend engineering and research infrastructure.
How to choose between Data Scientist and Machine Learning Engineer
- Pick Data Scientist if the route that produces the most career-changer hires is analyst first and Data Scientist second: 18 to 30 months of production experience substitutes for a graduate degree in a way no certificate does, and you are paid throughout. Going direct from zero means competing with master's graduates for the same junior slot.
- Pick Machine Learning Engineer if almost every Machine Learning Engineer hire in the United States comes from someone already employed as a software engineer, data engineer or data scientist who moved across after two to four years, usually inside the same company, because the interview loop tests production software engineering at the same bar as a backend engineer and that skill is built by doing the job rather than by coursework.
The natural next moves are machine learning engineer and AI engineer, which have absorbed most of the production modelling work and pay more, plus analytics and data science management. Both engineering moves raise the software bar (testing, services, deployment) rather than the statistics bar, and neither requires a graduate degree. The natural moves are AI Engineer, which is a faster door in because the bar sits closer to a strong application developer’s, and Data Engineer or Software Engineer, which pay less at the top but hire far more readily. Moving toward research or Applied Scientist titles raises the degree bar sharply, because those postings usually want a PhD; moving toward platform and infrastructure work does not, and pays comparably.
Data Scientist vs Machine Learning Engineer FAQ
Which pays more, Data Scientist or Machine Learning Engineer?
At mid-career the median is $140,000 for a Data Scientist and $200,000 for a Machine Learning Engineer; at senior level $180,000 versus $270,000. Entry medians are $110,000 and $140,000. Figures are US base plus typical bonus where reported, checked September 2026.
Is it faster to become a Data Scientist or a Machine Learning Engineer?
The quickest verified route into Data Scientist is Domain expert converting at about 12 months; for Machine Learning Engineer it is Data engineer to ML engineer at about 18 months. Our full roadmaps run 890 and 1,360 study hours respectively.
Which is harder to automate, Data Scientist or Machine Learning Engineer?
We rate automation exposure medium for Data Scientist and low for Machine Learning Engineer. Large language models already do exploratory analysis, boilerplate feature engineering and first-draft modelling code well, which erodes the junior end of Data Scientist work; the US Bureau of Labor Statistics cites the integration of AI into business workflows as a reason the occupation is projected to grow 35 percent to 2035. Experiment design, causal inference, spotting an artefact and persuading an organisation to act are not close to automated. Code assistants now write much of the boilerplate a Machine Learning Engineer used to type, along with first-draft pipelines, tests and documentation, which is why the same loops now include an AI-assisted coding round. What does not automate is scoping the problem, designing the evaluation that decides whether a model ships, and owning a system whose failures are statistical rather than a stack trace.
Do I need a certification for Data Scientist or Machine Learning Engineer?
No certification is required or expected for a Data Scientist job, and none of them substitutes for a degree or production experience. The most useful item on the list is the Machine Learning Specialization from Stanford Online and DeepLearning.AI at $49 a month for about two months and 95 hours, which is a curriculum rather than a credential. The Databricks Certified Machine Learning Associate at $200 is the only proctored exam here and it only matters if your target employers run Databricks; the DataCamp certifications carry little weight with hiring managers. No certification is required for a Machine Learning Engineer role and no employer screens on one. The credential with real signal is the Certified Kubernetes Administrator at $445, because it is purely performance-based on live clusters, cannot be passed from question dumps, and Kubernetes is where serious training and inference workloads run - note that it expires after two years and renewal means sitting the whole exam again at full price. The AWS Certified Machine Learning Engineer - Associate at $150 and the Coursera specializations from Andrew Ng and DeepLearning.AI are curricula worth the study time, not credentials anyone hires on.