Salary for Entry-level / Junior Applied Scientist in United States during 2023

Salary details

The average entry-level / junior Applied Scientist salary lies between USD 130,760 and USD 213,660 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. 17, 2024

Salary trend

Top 20 Job Tags for Entry-level / Junior Applied Scientist roles

The three most common job tag items assiciated with entry-level / junior Applied Scientist job listings are Machine Learning, Python and Research. 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:

Machine Learning | 15 jobs Python | 14 jobs Research | 14 jobs Statistics | 14 jobs Computer Science | 14 jobs Computer Vision | 12 jobs Engineering | 12 jobs Java | 12 jobs Robotics | 11 jobs Deep Learning | 9 jobs PhD | 9 jobs Mathematics | 8 jobs R | 3 jobs Data Mining | 3 jobs NLP | 2 jobs PyTorch | 2 jobs Matlab | 2 jobs AWS | 2 jobs Data Analytics | 2 jobs Industrial | 2 jobs

Top 20 Job Perks/Benefits for Entry-level / Junior Applied Scientist roles

The three most common job benefits and perks assiciated with entry-level / junior Applied Scientist job listings are Career development, Conferences 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 | 6 jobs Conferences | 6 jobs Equity / stock options | 5 jobs Flex hours | 1 jobs Startup environment | 1 jobs Team events | 1 jobs

Salary Composition

The salary for an entry-level or junior applied scientist in AI/ML/Data Science typically consists of 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 makes up the majority of the total compensation package. Performance bonuses can vary significantly depending on the company’s policies and your individual performance. Additional remuneration, like stock options, is more common in larger tech companies or startups and can be a significant part of the total compensation, especially in regions like Silicon Valley. In contrast, companies in other regions or industries might offer less in terms of equity but could compensate with higher base salaries or bonuses.

Increasing Salary

To increase your salary from an entry-level position, consider the following steps:

• Skill Enhancement: Continuously upgrade your skills in AI/ML through advanced courses or certifications. Specializing in high-demand areas like deep learning, natural language processing, or computer vision can make you more valuable.
• Networking: Build a strong professional network by attending industry conferences, meetups, and online forums. Networking can open up opportunities for higher-paying positions.
• Performance and Impact: Demonstrate your impact on projects and the company’s bottom line. Quantifiable achievements can be a strong case for salary negotiations.
• Advanced Education: Pursuing a master’s or Ph.D. in a relevant field can open doors to higher-level positions and salaries.
• Switching Companies: Sometimes, moving to a different company can result in a significant salary increase, especially if the new company values your skills and experience more.

Educational Requirements

Most entry-level applied scientist roles require at least a bachelor’s degree in a relevant field such as computer science, data science, mathematics, or engineering. However, many employers prefer candidates with a master’s degree or even a Ph.D., especially for roles that involve complex problem-solving and research. A strong academic background in statistics, machine learning, and programming is often essential.

Helpful Certifications

While not always required, certain certifications can enhance your resume and demonstrate your commitment to the field. Some valuable certifications include:

• Certified Machine Learning Professional (CMLP)
• TensorFlow Developer Certificate
• AWS Certified Machine Learning – Specialty
• Microsoft Certified: Azure AI Engineer Associate

These certifications can validate your skills and knowledge in specific tools and platforms, making you more attractive to potential employers.

Required Experience

For entry-level positions, employers typically look for candidates with some practical experience, which can be gained through internships, co-op programs, or relevant projects during your studies. Experience with programming languages like Python or R, and familiarity with machine learning frameworks such as TensorFlow or PyTorch, is often expected. Additionally, experience with data analysis and visualization tools can be beneficial.

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