Salary for Entry-level / Junior Data Scientist in United States during 2021

Salary details

The average entry-level / junior Data Scientist salary lies between USD 80,000 and USD 100,000 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: Dec. 29, 2024

Salary trend

Salary Composition

The salary for an entry-level or junior data scientist in the United States typically consists of a base salary, performance bonuses, and sometimes additional remuneration such as stock options or benefits. 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 the individual's performance. Additional remuneration might include stock options, especially in tech companies, or other benefits like health insurance, retirement plans, and paid time off.

The composition of the salary can vary based on several factors:

• Region: Salaries in tech hubs like San Francisco, New York, and Seattle tend to be higher due to the cost of living and demand for talent.
• Industry: Tech companies often offer higher salaries and more stock options compared to other industries like finance or healthcare.
• Company Size: Larger companies may offer more comprehensive benefits and bonuses, while startups might offer equity as part of the compensation package.

Increasing Salary

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

• Skill Development: Continuously improve your technical skills, especially in programming languages like Python or R, and tools like TensorFlow or PyTorch.
• Specialization: Develop expertise in a niche area of data science, such as natural language processing, computer vision, or big data analytics.
• Advanced Education: Pursue a master's degree or Ph.D. in data science, computer science, or a related field to enhance your qualifications.
• Networking: Build a professional network by attending industry conferences, joining data science communities, and connecting with professionals on LinkedIn.
• Performance: Consistently exceed performance expectations in your current role to position yourself for promotions and salary increases.

Educational Requirements

Most entry-level data scientist positions require at least a bachelor's degree in a relevant field such as data science, computer science, statistics, mathematics, or engineering. Some employers may prefer candidates with a master's degree, especially for roles that require more advanced analytical skills. Coursework in machine learning, data mining, statistical analysis, and programming is highly beneficial.

Helpful Certificates

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

• Certified Analytics Professional (CAP)
• Google Professional Data Engineer
• Microsoft Certified: Azure Data Scientist Associate
• IBM Data Science Professional Certificate
• AWS Certified Machine Learning – Specialty

These certifications can validate your skills and knowledge in data science and machine learning, making you a more attractive candidate to potential employers.

Experience Requirements

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. Experience with data analysis, machine learning models, and data visualization tools is often expected. Demonstrating experience through a portfolio of projects or contributions to open-source projects can also be beneficial.

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