Data science salaries remain strong as companies invest heavily in AI and data infrastructure. ML engineers and AI specialists command the highest premiums.
Browse 19 open data science jobs →
| Role | Entry Level | Mid Level | Senior |
|---|---|---|---|
| Data Analyst | $55k–$75k | $80k–$110k | $115k–$150k |
| Data Scientist | $75k–$100k | $105k–$155k | $160k–$220k |
| ML Engineer | $85k–$115k | $120k–$175k | $180k–$260k |
| Data Engineer | $70k–$95k | $100k–$150k | $155k–$220k |
| AI Researcher | $100k–$140k | $145k–$200k | $210k–$350k+ |
Data scientists in the US earn an average of $110k–$155k. Entry-level data scientists start at $75k–$100k. Mid-level scientists earn $105k–$155k. Senior data scientists and ML engineers earn $160k–$250k+. AI researchers at top labs (OpenAI, Google DeepMind, Anthropic) can earn $300k–$1M+ in total compensation.
Yes, but the role has evolved. Routine data analysis is increasingly automated, raising the bar for what data scientists are expected to do. The most in-demand skills are now around LLMs, RAG systems, MLOps, and productionizing AI. Data scientists who understand the full ML lifecycle — from data ingestion to model deployment — are the most valuable.
Data scientists build models and derive insights from data — heavy on statistics and Python. ML engineers take those models and build the systems to train, deploy, and monitor them at scale — more software engineering-heavy. Data engineers build the data infrastructure (pipelines, warehouses, lakes) that everyone else depends on — heavy on SQL, Spark, and cloud platforms.
In 2026, the highest-premium skills are: (1) LLM fine-tuning and RAG (retrieval-augmented generation), (2) MLOps and model deployment (Kubernetes, Docker, MLflow), (3) PyTorch and deep learning, (4) cloud platforms (AWS SageMaker, Google Vertex AI), and (5) strong SQL and data engineering fundamentals.
Only for research-focused roles at top AI labs. The vast majority of industry data science positions (including at Google, Meta, and Netflix) hire MS-level and even BS-level candidates with strong portfolios. What matters is demonstrable ability to solve business problems with data and build production-ready systems.