Salary for Entry-level / Junior Applied Scientist during 2024

Salary details

The average entry-level / junior Applied Scientist salary lies between USD 104,000 and USD 168,150 globally. 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.

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, Research and Python. Below you find a list of the 20 most occuring job tags in 2024 and the number of open jobs that where associated with them during that period:

Machine Learning | 50 jobs Research | 50 jobs Python | 43 jobs Computer Science | 43 jobs Engineering | 39 jobs Deep Learning | 38 jobs Statistics | 34 jobs PhD | 31 jobs NLP | 25 jobs LLMs | 24 jobs PyTorch | 22 jobs Computer Vision | 20 jobs Java | 20 jobs TensorFlow | 17 jobs ML models | 15 jobs Data Mining | 13 jobs Mathematics | 13 jobs GitHub | 11 jobs Econometrics | 10 jobs EMNLP | 10 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 Medical leave. Below you find a list of the 20 most occuring job perks or benefits in 2024 and the number of open jobs that where offering them during that period:

Career development | 52 jobs Conferences | 27 jobs Medical leave | 26 jobs Flex hours | 23 jobs Health care | 17 jobs Startup environment | 17 jobs Flex vacation | 15 jobs Equity / stock options | 14 jobs Parental leave | 14 jobs Competitive pay | 13 jobs Team events | 7 jobs 401(k) matching | 6 jobs Insurance | 5 jobs Flexible spending account | 5 jobs Transparency | 3 jobs Home office stipend | 3 jobs Salary bonus | 2 jobs Wellness | 1 jobs Gear | 1 jobs Signing bonus | 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. In larger tech companies or startups, stock options or equity can be a significant part of the compensation, offering long-term financial benefits. 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 compared to academia or non-profits. Company size can influence the package as well, with larger companies often providing more comprehensive benefits and smaller companies offering more equity.

Increasing Salary

To increase your salary from an entry-level position, consider gaining specialized skills or certifications that are in high demand, such as deep learning, natural language processing, or cloud computing. Pursuing a master's or Ph.D. in a relevant field can also enhance your qualifications and open up higher-paying opportunities. Networking within the industry and seeking mentorship can provide insights into career advancement opportunities. Additionally, gaining experience in managing projects or leading teams can position you for roles with greater responsibility and higher pay. Transitioning to a company or industry known for higher compensation, such as finance or tech giants, can also be a strategic move.

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, a master's degree or Ph.D. is often preferred, especially for roles that involve complex problem-solving and research. These advanced degrees provide a deeper understanding of machine learning algorithms, statistical methods, and data analysis techniques, which are crucial for the role.

Helpful Certifications

While not always required, certain certifications can enhance your resume and demonstrate your expertise to potential employers. Certifications in machine learning from platforms like Coursera, edX, or Udacity can be beneficial. Google’s Professional Machine Learning Engineer certification or AWS Certified Machine Learning – Specialty are also well-regarded in the industry. These certifications validate your skills in designing, building, and deploying machine learning models.

Required Experience

For an entry-level position, 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 such as Python or R, and familiarity with machine learning frameworks like TensorFlow or PyTorch, is often expected. While direct industry experience might not be mandatory, demonstrating your ability to apply theoretical knowledge to real-world problems is crucial.

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