By Saeed Mirshekari
June 12, 2025
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Top Paying Companies for Data Scientists and Data/ML Engineers in 2025
Data science and machine learning engineering have become some of the most sought-after and lucrative careers worldwide. Organizations across industries—from tech giants to financial firms—are investing heavily in data expertise to gain competitive advantages. If you’re a data scientist or an ML engineer looking for your next opportunity, one of your biggest questions likely is:
Which companies pay the best salaries for data science and ML roles?
In this comprehensive blog, we’ll explore:
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The key drivers behind high compensation in data roles
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Top paying companies and what makes them attractive
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How compensation packages break down (base, bonuses, equity)
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Sector-specific variations
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Strategies to land roles at these companies
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Realistic expectations for your career growth and pay
Let’s dive in.
Why Do Some Companies Pay More?
Before listing companies, it’s important to understand why some pay more than others. Data science and ML engineering are specialized skills with high demand and relatively limited supply. Factors influencing pay include:
1. Industry and Market Position
Tech giants like Google, Meta, Amazon, and Apple dominate due to massive revenues and huge data needs. FinTech, healthcare, and consulting firms also offer competitive pay but vary by scale.
2. Revenue and Funding
Startups with significant funding (e.g., unicorns) pay handsomely to attract talent, often with a mix of base and equity.
3. Talent Competition
Companies competing for top-tier talent drive salaries up. Highly visible companies tend to have strong employer branding and aggressive compensation.
4. Role Complexity and Impact
Positions requiring cutting-edge ML research or mission-critical production systems often pay more.
5. Location
Salaries vary widely based on geography—Silicon Valley and NYC typically lead, though remote work is changing this.
The Top Paying Companies in 2025
1. Google (Alphabet)
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Typical roles: Data Scientist, ML Engineer, Research Scientist
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Average base salary: $140,000–$180,000
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Total comp (including bonuses & equity): $180,000–$300,000+
Why Google?
Google’s vast data infrastructure, search engine, ads platform, cloud business, and AI labs (DeepMind, Brain) require deep expertise. Compensation packages include significant stock grants and bonuses, reflecting its high valuation and profitability.
Notable perks:
2. Meta (Facebook)
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Typical roles: Data Scientist, ML Engineer, Applied Scientist
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Average base salary: $140,000–$190,000
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Total comp: $200,000–$350,000+
Why Meta?
Meta is deeply invested in AI, personalization, content ranking, and the Metaverse. They pay top dollar to attract talent who can innovate in large-scale ML systems and recommendation algorithms.
Bonus: Meta offers high RSU (restricted stock units) grants which contribute to overall comp.
3. Amazon
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Typical roles: Data Scientist, Data Engineer, ML Engineer
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Average base salary: $130,000–$170,000
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Total comp: $170,000–$300,000+
Why Amazon?
Amazon’s e-commerce, AWS cloud, and Alexa voice assistant generate vast amounts of data. Amazon also emphasizes leadership principles and customer obsession, which means ML engineers work on practical and impactful systems.
Note: Amazon’s compensation mix leans heavily on stock vesting over 4 years.
4. Apple
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Typical roles: Data Scientist, ML Engineer, AI Researcher
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Average base salary: $140,000–$180,000
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Total comp: $180,000–$300,000+
Apple focuses heavily on user privacy, hardware AI integration, and personal device intelligence. Their data science roles often overlap with hardware and software innovation.
5. Netflix
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Typical roles: Data Scientist, ML Engineer, Personalization Scientist
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Average base salary: $150,000–$200,000
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Total comp: $220,000–$350,000+
Netflix’s data teams work on recommendation systems, streaming quality, A/B testing, and customer retention analytics. They are known for paying above market rates and having a “high performance, high freedom” culture.
6. Microsoft
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Typical roles: Data Scientist, ML Engineer, AI Researcher
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Average base salary: $130,000–$170,000
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Total comp: $170,000–$280,000+
Microsoft’s investments in cloud computing (Azure), productivity software, and AI research drive the need for talented data scientists and ML engineers.
7. LinkedIn
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Typical roles: Data Scientist, ML Engineer, Research Scientist
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Average base salary: $130,000–$160,000
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Total comp: $160,000–$280,000+
LinkedIn leverages data science for job recommendations, advertising, and user engagement. Owned by Microsoft, it offers competitive compensation.
8. Stripe
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Typical roles: Data Scientist, ML Engineer, Financial Analyst
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Average base salary: $150,000–$190,000
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Total comp: $200,000–$350,000+
As a leading FinTech company, Stripe offers data roles centered on fraud detection, payment optimization, and risk modeling. They offer high salaries and generous equity packages.
9. Airbnb
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Typical roles: Data Scientist, ML Engineer
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Average base salary: $140,000–$180,000
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Total comp: $180,000–$320,000+
Airbnb’s data teams focus on pricing models, user experience optimization, and trust & safety systems.
10. Salesforce
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Typical roles: Data Scientist, ML Engineer
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Average base salary: $130,000–$165,000
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Total comp: $160,000–$280,000+
Salesforce integrates AI into CRM products, requiring strong ML expertise.
Sector-Specific Salary Insights
| Sector |
Average Total Compensation for Senior Data Scientist/ML Engineer |
| Tech Giants |
$180,000 – $350,000+ |
| FinTech |
$150,000 – $300,000 |
| Healthcare AI |
$140,000 – $250,000 |
| Consulting |
$120,000 – $200,000 |
| Startups |
$120,000 – $280,000 (varies widely with equity) |
What Does Compensation Include?
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Base Salary: Fixed annual salary
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Bonuses: Annual or quarterly cash bonuses tied to performance
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Equity: Stock options or RSUs, potentially the largest portion for senior roles
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Benefits: Health insurance, 401(k), wellness, etc.
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Perks: Remote work, relocation, education reimbursement
How to Get Into These Top Paying Companies
Landing a high-paying data role requires more than technical skills:
1. Build a solid portfolio
Projects that demonstrate ML pipelines, data cleaning, feature engineering, and model deployment.
2. Master the interview process
Prepare for coding, SQL, ML theory, system design, and behavioral questions.
3. Network strategically
Engage with current employees on LinkedIn, attend conferences, or participate in mentorship platforms like O’Mentors.
4. Keep skills current
Specialize in areas like deep learning, NLP, or big data technologies.
Geographic Factors
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Bay Area / Seattle / NYC: Highest paying regions
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Remote Work: Some companies pay equally; others adjust salaries based on cost of living
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Emerging Tech Hubs: Austin, Boston, Toronto offer growing opportunities
Trends Shaping Future Compensation
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AI boom: Generative AI and LLM-focused roles command premium
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Data Engineering rise: Data ops and ML engineering skills are highly rewarded
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Cross-functional skills: Product sense, communication, and leadership skills increase value
Summary Table: Top Paying Companies & Expected Comp Ranges
| Company |
Base Salary Range |
Total Comp Range (Including Equity & Bonus) |
| Google |
$140K – $180K |
$180K – $300K+ |
| Meta |
$140K – $190K |
$200K – $350K+ |
| Amazon |
$130K – $170K |
$170K – $300K+ |
| Apple |
$140K – $180K |
$180K – $300K+ |
| Netflix |
$150K – $200K |
$220K – $350K+ |
| Microsoft |
$130K – $170K |
$170K – $280K+ |
| LinkedIn |
$130K – $160K |
$160K – $280K+ |
| Stripe |
$150K – $190K |
$200K – $350K+ |
| Airbnb |
$140K – $180K |
$180K – $320K+ |
| Salesforce |
$130K – $165K |
$160K – $280K+ |
Final Thoughts
Choosing the right company to work for as a data scientist or ML engineer isn’t only about salary — it’s about culture, career growth, and impact. But compensation is a crucial factor, especially early in your career or when planning your financial future.
By targeting these top paying companies and investing in the right skills and preparation, you can accelerate your career and earnings significantly.
Remember: the best salaries come to those who not only code well but also understand business problems, communicate effectively, and innovate continuously.
If you want help navigating this competitive market, exploring mentorship with seasoned professionals, or sharpening your interview skills, check out O’Mentors.com.
Unlock your potential, land your dream job, and command the salary you deserve.