Salary for Entry-level / Junior Data Scientist during 2022

Salary details

The average entry-level / junior Data Scientist salary lies between USD 39,925 and USD 100,000 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 Data Scientist roles

The three most common job tag items assiciated with entry-level / junior Data Scientist job listings are Python, Machine Learning and Statistics. Below you find a list of the 20 most occuring job tags in 2022 and the number of open jobs that where associated with them during that period:

Python | 171 jobs Machine Learning | 146 jobs Statistics | 105 jobs Engineering | 100 jobs SQL | 90 jobs R | 84 jobs Mathematics | 79 jobs Computer Science | 76 jobs Research | 65 jobs Pandas | 47 jobs Deep Learning | 46 jobs Big Data | 45 jobs ML models | 45 jobs NumPy | 39 jobs TensorFlow | 37 jobs Data analysis | 37 jobs AWS | 35 jobs Scikit-learn | 33 jobs Spark | 33 jobs NLP | 32 jobs

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

The three most common job benefits and perks assiciated with entry-level / junior Data Scientist job listings are Career development, Startup environment and Flex hours. Below you find a list of the 20 most occuring job perks or benefits in 2022 and the number of open jobs that where offering them during that period:

Career development | 119 jobs Startup environment | 52 jobs Flex hours | 38 jobs Health care | 33 jobs Team events | 32 jobs Competitive pay | 27 jobs Flex vacation | 21 jobs Equity / stock options | 19 jobs Insurance | 16 jobs Conferences | 11 jobs Salary bonus | 10 jobs Snacks / Drinks | 8 jobs Parental leave | 7 jobs Wellness | 7 jobs Medical leave | 7 jobs Home office stipend | 6 jobs Gear | 5 jobs 401(k) matching | 4 jobs Lunch / meals | 4 jobs Relocation support | 4 jobs

Salary Composition

The salary for an entry-level or junior data scientist typically consists of a fixed base salary, which forms the bulk of the compensation package. In addition to the base salary, many companies offer performance-based bonuses, which can vary significantly depending on the company's financial health, the individual's performance, and the industry. For instance, tech companies and financial institutions might offer higher bonuses compared to non-tech industries. Additional remuneration can include stock options, especially in startups or large tech firms, and benefits such as health insurance, retirement plans, and sometimes even educational stipends. The composition can also vary by region, with tech hubs like Silicon Valley or New York City offering higher base salaries and bonuses compared to other regions due to the higher cost of living and competitive job market.

Increasing Salary

To increase your salary from an entry-level position, consider gaining specialized skills that are in high demand, such as expertise in machine learning algorithms, proficiency in programming languages like Python or R, or experience with big data technologies like Hadoop or Spark. Pursuing advanced education, such as a master's degree or relevant certifications, can also make you more competitive. Networking within the industry and seeking mentorship can provide insights into career advancement opportunities. Additionally, gaining experience in a specific industry, such as finance, healthcare, or e-commerce, can make you more valuable to employers in those sectors. Demonstrating leadership skills and taking on more responsibilities can also position you for promotions and salary increases.

Educational Requirements

Most entry-level data scientist positions require at least a bachelor's degree in a related field such as computer science, statistics, mathematics, or engineering. However, many employers prefer candidates with a master's degree in data science or a related discipline, as it provides a deeper understanding of the field and more hands-on experience with data analysis and machine learning techniques. Some positions may also consider candidates with degrees in other quantitative fields, provided they have relevant skills and experience.

Helpful Certifications

Certifications can be a valuable addition to your resume, demonstrating your commitment to the field and your expertise in specific areas. Some popular certifications for data scientists include:

• Certified Analytics Professional (CAP)
• Microsoft Certified: Azure Data Scientist Associate
• Google Professional Data Engineer
• IBM Data Science Professional Certificate
• TensorFlow Developer Certificate

These certifications can help you stand out in the job market and may be particularly beneficial if you lack formal education in data science.

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 data analysis, statistical modeling, and machine learning projects is highly valued. Familiarity with data visualization tools and programming languages like Python, R, or SQL is often required. While extensive professional experience is not expected for entry-level roles, demonstrating your ability to apply theoretical knowledge to real-world problems can be a significant advantage.

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