Data Scientist
$113,333
avg. annual salary
Ai/ml Engineer
pays more on average
Ai/ml Engineer
$119,862
avg. annual salary
In today’s technology-driven landscape, both Data Scientists and AI/ML Engineers are pivotal to the success of organizations. While these roles share a common foundation in data expertise and analytical skills, they often diverge in terms of responsibilities, salary expectations, and career trajectories. This guide delves into the financial aspects, detailing the average salaries, job benefits, and potential career growth for both roles. By understanding these differences, you can make a more informed decision about which career path may best suit your interests and financial goals. Let’s explore what you can expect from each profession, the skills that drive salary variations, and the evolving demand for these high-stakes positions in the tech world.
Salary by Experience Level
starting salary
starting salary
avg. difference (6%)
Average Salaries
As of 2025, the average salary for a Data Scientist is approximately $120,000, with a typical range from $100,000 to $150,000, depending on experience and location. In contrast, AI/ML Engineers earn an average of $130,000, with salaries ranging from $110,000 to $160,000.
These roles are among the most sought after in the tech industry, and specialization can further influence earning potential.
Salary by Experience Level
Entry-level Data Scientists can expect to earn between $80,000 and $100,000, while Mid-level professionals might see salaries around $110,000 to $130,000. Senior Data Scientists typically make $150,000 or more.
For AI/ML Engineers, entry-level salaries range from $90,000 to $110,000, with Mid-level roles commanding $120,000 to $140,000, and Senior engineers often exceed $160,000.
Job Benefits
Both Data Scientists and AI/ML Engineers enjoy competitive benefits that often include health insurance, retirement plans, and performance bonuses. Employers may also offer opportunities for continuing education, professional development, and flexible working arrangements to attract top talent.
Career Paths
Data Scientists typically evolve into roles such as Senior Data Scientist, Data Science Manager, or Chief Data Officer. Meanwhile, AI/ML Engineers may advance to positions like Senior AI Engineer, Machine Learning Architect, or Project Lead.
The skills developed in these roles also enable transitions into management or specialized technical positions.
Industry Demand
The demand for both professions is on the rise, driven by exponential data growth and a need for advanced analytical capabilities. While both roles present lucrative opportunities, the rapid evolution of AI technologies gives AI/ML Engineers a unique edge in emerging industry sectors.
Detailed Comparison
### Side-by-side salary and role snapshot
- •Salary ranges (U.S.): Entry-level Data Scientist $80K–$110K; Senior Data Scientist $140K–$200K. Entry-level AI/ML Engineer $95K–$130K; Senior AI/ML Engineer $160K–$240K. On average, ML engineers earn about 10–25% more in base pay.
- •Total compensation: At large tech firms, senior ML engineers often hit $300K+ total comp (salary + bonus + equity); senior data scientists commonly reach $220K–$280K.
- •Skill differences: Data scientists focus on statistics, A/B testing, and dashboards. ML engineers emphasize model productionization, scalable systems, and software engineering.
- •Location impact: SF/NYC premiums add ~20–40% to base pay.
Actionable takeaway: Match role to strengths—software engineering plus deployment favors ML engineering; experimentation and insights favor data science.
Key Factors That Affect Salary
### Main variables that change pay
- •Location: Coastal cities pay 20–40% more; remote roles can pay 5–15% less depending on company policy.
- •Experience and title: Each level up (junior → mid → senior) can increase pay by ~25–40%.
- •Education: MS typically adds 10–15% over a BS; a PhD can add another ~10–15% depending on the role.
- •Industry and domain: Finance and healthcare roles often pay 5–15% more than retail or education.
- •Technical stack and impact: Expertise in PyTorch/TensorFlow, distributed systems, and MLOps raises offers; demonstrable product impact (revenue or cost savings) boosts negotiation leverage.
Actionable takeaway: Quantify your impact (metrics, $ saved/earned) and ask for 10–20% above your target when negotiating.