Salary for Mid-level / Intermediate Data Scientist in United Kingdom during 2024

Salary details

The average mid-level / intermediate Data Scientist salary lies between USD 47,706 and USD 87,500 in the United Kingdom. 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. 15, 2024

Salary trend

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

The three most common job tag items assiciated with mid-level / intermediate Data Scientist job listings are Python, Statistics and Machine Learning. 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:

Python | 3143 jobs Statistics | 2779 jobs Machine Learning | 2739 jobs SQL | 2416 jobs Engineering | 2171 jobs Computer Science | 1868 jobs R | 1839 jobs Mathematics | 1645 jobs Research | 1518 jobs Data analysis | 1261 jobs ML models | 1086 jobs Testing | 925 jobs AWS | 918 jobs Data visualization | 867 jobs Tableau | 798 jobs Pipelines | 771 jobs Spark | 730 jobs NLP | 721 jobs Big Data | 682 jobs Security | 673 jobs

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

The three most common job benefits and perks assiciated with mid-level / intermediate Data 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 2024 and the number of open jobs that where offering them during that period:

Career development | 2572 jobs Health care | 1335 jobs Equity / stock options | 878 jobs Flex hours | 875 jobs Competitive pay | 684 jobs Startup environment | 620 jobs Insurance | 620 jobs Medical leave | 607 jobs Parental leave | 561 jobs Team events | 537 jobs Salary bonus | 521 jobs Flex vacation | 509 jobs 401(k) matching | 391 jobs Wellness | 358 jobs Conferences | 158 jobs Unlimited paid time off | 135 jobs Flexible spending account | 134 jobs Relocation support | 119 jobs Transparency | 114 jobs Fitness / gym | 112 jobs

Salary Composition for a Mid-level Data Scientist in the UK

The salary for a mid-level data scientist in the UK typically comprises a fixed base salary, performance bonuses, and additional remuneration such as stock options or benefits. The fixed base salary is the most substantial component, often accounting for 70-85% of the total compensation package. Performance bonuses can vary significantly depending on the company and industry, ranging from 5-20% of the base salary. Additional remuneration, such as stock options, profit-sharing, or benefits like health insurance and retirement contributions, can make up the remaining 5-10%.

Regional differences also play a role; for instance, salaries in London are generally higher due to the cost of living and the concentration of tech companies. Industry-wise, sectors like finance and technology tend to offer higher compensation packages compared to academia or non-profit organizations. Larger companies may provide more comprehensive benefits and bonuses, while startups might offer equity as part of the compensation package.

Steps to Increase Salary from a Mid-level Position

To increase your salary from a mid-level data scientist position, consider the following strategies:

• Skill Enhancement: Continuously update and expand your technical skills, particularly in high-demand areas like machine learning, deep learning, and big data technologies.
• Advanced Education: Pursuing a master's degree or a Ph.D. in data science or a related field can open up higher-paying opportunities.
• Specialization: Develop expertise in a niche area, such as natural language processing or computer vision, which can make you more valuable to employers.
• Leadership Roles: Seek opportunities to lead projects or teams, as managerial roles often come with higher pay.
• Networking: Build a strong professional network to learn about higher-paying opportunities and gain insights into industry trends.
• Certifications: Obtain relevant certifications that can validate your skills and potentially lead to salary increases.

Educational Requirements for a Mid-level Data Scientist

Most mid-level data scientist positions require at least a bachelor's degree in a relevant field such as computer science, statistics, mathematics, or engineering. However, many employers prefer candidates with a master's degree in data science, analytics, or a related discipline. A strong foundation in statistical analysis, programming, and data manipulation is essential. Coursework or experience in machine learning, data visualization, and database management is also highly beneficial.

Helpful Certifications for Data Scientists

While not always mandatory, certain certifications can enhance your credentials and demonstrate your expertise to potential employers. Some valuable certifications include:

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

These certifications can help validate your skills in specific tools and methodologies, making you a more competitive candidate.

Experience Required for Mid-level Data Scientist Roles

Typically, a mid-level data scientist is expected to have 2-5 years of relevant experience. This experience should include hands-on work with data analysis, statistical modeling, and machine learning. Experience with programming languages such as Python or R, as well as familiarity with data visualization tools like Tableau or Power BI, is often required. Additionally, experience in a specific industry can be advantageous, as it provides context and understanding of domain-specific challenges.

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