Salary for Mid-level / Intermediate Research Scientist in United States during 2023

Salary details

The average mid-level / intermediate Research Scientist salary lies between USD 130,000 and USD 210,000 in the United States. It represents the overall compensation/gross salary amount for the working year (before deductions like social security, taxes and other contributions), not including equity/stock options or similar benefits.

Region represents the primary country of residence of an employee during the year (or residence for tax purposes). All data shown are full-time equivalent (FTE) salaries. Part-time salary information has been extrapolated to its FTE value.

Last updated: Nov. 24, 2024

Salary trend

Top 20 Job Tags for Mid-level / Intermediate Research Scientist roles

The three most common job tag items assiciated with mid-level / intermediate Research Scientist job listings are Research, Machine Learning and PhD. Below you find a list of the 20 most occuring job tags in 2023 and the number of open jobs that where associated with them during that period:

Research | 56 jobs Machine Learning | 41 jobs PhD | 32 jobs Engineering | 29 jobs Python | 27 jobs Deep Learning | 27 jobs Computer Science | 26 jobs PyTorch | 24 jobs R | 23 jobs TensorFlow | 21 jobs Statistics | 20 jobs R&D | 16 jobs Computer Vision | 14 jobs Mathematics | 13 jobs Physics | 13 jobs Chemistry | 11 jobs NLP | 10 jobs Biology | 10 jobs Security | 9 jobs Data analysis | 9 jobs

Top 20 Job Perks/Benefits for Mid-level / Intermediate Research Scientist roles

The three most common job benefits and perks assiciated with mid-level / intermediate Research Scientist job listings are Career development, Health care and Equity / stock options. Below you find a list of the 20 most occuring job perks or benefits in 2023 and the number of open jobs that where offering them during that period:

Career development | 49 jobs Health care | 35 jobs Equity / stock options | 24 jobs Startup environment | 23 jobs Insurance | 22 jobs Conferences | 20 jobs Flex hours | 18 jobs Competitive pay | 16 jobs Flex vacation | 15 jobs Parental leave | 13 jobs 401(k) matching | 11 jobs Wellness | 11 jobs Team events | 11 jobs Medical leave | 10 jobs Salary bonus | 5 jobs Gear | 4 jobs Fitness / gym | 3 jobs Relocation support | 3 jobs Home office stipend | 3 jobs Travel | 2 jobs

Salary Composition

In the United States, the salary for a mid-level/intermediate research scientist in AI/ML/Data Science typically comprises a base salary, performance bonuses, and additional remuneration such as stock options or equity, especially in tech companies. The base salary is the fixed component and usually constitutes the majority of the total compensation package. Performance bonuses can vary significantly depending on the company's profitability and individual performance, often ranging from 10% to 20% of the base salary. Additional remuneration, such as stock options, is more common in larger tech companies and startups, providing long-term incentives aligned with company growth. The composition can vary by region, with tech hubs like Silicon Valley offering higher base salaries and more substantial equity packages compared to other regions. Industry also plays a role; for instance, finance and healthcare sectors might offer higher bonuses, while tech companies might provide more in stock options. Company size can influence the package, with larger companies offering more structured bonuses and smaller companies offering more equity.

Increasing Salary

To increase your salary from a mid-level position, consider pursuing advanced roles such as Senior Research Scientist or Lead Data Scientist. This often requires demonstrating leadership skills, taking on more complex projects, and contributing to strategic decision-making. Networking within the industry and building a strong professional brand can open opportunities for higher-paying roles. Additionally, specializing in high-demand areas like deep learning, natural language processing, or AI ethics can make you more valuable. Pursuing further education, such as a Ph.D. or an MBA, can also enhance your qualifications and salary prospects. Negotiating skills are crucial; understanding your market value and effectively communicating your contributions can lead to better compensation packages.

Educational Requirements

Most mid-level research scientist positions in AI/ML/Data Science require at least a master's degree in a relevant field such as computer science, data science, statistics, or mathematics. A Ph.D. is often preferred, especially for research-intensive roles, as it demonstrates a deep understanding of complex concepts and the ability to conduct independent research. Candidates with a strong academic background in machine learning, artificial intelligence, or a related discipline are highly sought after. Practical experience through projects, internships, or previous roles is also essential to complement formal education.

Helpful Certifications

While not always mandatory, certain certifications can enhance your profile and demonstrate expertise in specific areas. Certifications such as the TensorFlow Developer Certificate, AWS Certified Machine Learning – Specialty, or the Microsoft Certified: Azure AI Engineer Associate can be beneficial. These certifications validate your skills in using specific tools and platforms, which can be advantageous in roles that require proficiency in these technologies. Additionally, completing courses from platforms like Coursera, edX, or Udacity in specialized topics can also be valuable.

Required Experience

Typically, a mid-level research scientist role requires 3-5 years of experience in AI/ML or data science. This experience should include hands-on work with machine learning models, data analysis, and programming in languages such as Python or R. Experience in deploying models in production environments and working with large datasets is often expected. Prior experience in a research setting, whether academic or industry-based, is highly beneficial, as it demonstrates the ability to conduct experiments, analyze results, and contribute to scientific knowledge.

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