Salary for Mid-level / Intermediate Decision Scientist during 2024
💰 The median Salary for Mid-level / Intermediate Decision Scientist during 2024 is USD 129,000
✏️ This salary info is based on 32 individual salaries reported during 2024
Salary details
The average mid-level / intermediate Decision Scientist salary lies between USD 82,915 and USD 150,200 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.
- Job title
- Decision Scientist
- Experience
- Mid-level / Intermediate
- Region
- global/worldwide
- Salary year
- 2024
- Sample size
- 32
- Top 10%
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- Top 25%
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- Median
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- Bottom 25%
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- Bottom 10%
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All data shown are full-time equivalent (FTE) salaries. Part-time salary information has been extrapolated to its FTE value.
Last updated:Salary trend
Top 20 Job Tags for Mid-level / Intermediate Decision Scientist roles
The three most common job tag items assiciated with mid-level / intermediate Decision Scientist job listings are Python, SQL and Statistics. 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 | 33 jobs SQL | 33 jobs Statistics | 29 jobs Engineering | 24 jobs Machine Learning | 18 jobs Mathematics | 18 jobs R | 16 jobs Economics | 15 jobs Tableau | 14 jobs Computer Science | 14 jobs Research | 13 jobs Testing | 10 jobs Power BI | 9 jobs CX | 9 jobs Data visualization | 9 jobs Data Analytics | 8 jobs Consulting | 7 jobs Excel | 7 jobs Data analysis | 7 jobs Matplotlib | 6 jobsTop 20 Job Perks/Benefits for Mid-level / Intermediate Decision Scientist roles
The three most common job benefits and perks assiciated with mid-level / intermediate Decision Scientist job listings are Career development, Equity / stock options and Health care. 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 | 29 jobs Equity / stock options | 23 jobs Health care | 17 jobs Competitive pay | 11 jobs Team events | 8 jobs Salary bonus | 8 jobs Flex hours | 7 jobs Startup environment | 7 jobs Insurance | 7 jobs Parental leave | 6 jobs Relocation support | 5 jobs Medical leave | 5 jobs Unlimited paid time off | 5 jobs 401(k) matching | 4 jobs Wellness | 4 jobs Flex vacation | 2 jobs Snacks / Drinks | 2 jobs Flexible spending account | 2 jobs Flat hierarchy | 1 jobs Conferences | 1 jobsSalary Composition
The salary for a Mid-level/Intermediate Decision Scientist typically comprises a base salary, performance bonuses, and additional remuneration such as stock options or benefits. The base salary often constitutes the majority of the total compensation package, usually around 70-80%. Performance bonuses can vary significantly depending on the company and industry, ranging from 10-20% of the total compensation. Additional remuneration, such as stock options, profit-sharing, or other benefits, can make up the remaining 5-10%.
Regional differences also play a significant role; for instance, salaries in tech hubs like Silicon Valley or New York City tend to be higher due to the cost of living and competitive job market. Industry-wise, tech companies and financial services often offer higher compensation packages compared to academia or non-profit sectors. Larger companies may provide more comprehensive benefits and bonuses, while smaller companies might offer more equity or stock options.
Increasing Salary
To increase your salary from a Mid-level Decision Scientist position, consider the following strategies:
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Skill Enhancement: Continuously update and expand your skill set, particularly in emerging technologies and methodologies in AI/ML and data science. Specializing in niche areas can make you more valuable.
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Advanced Education: Pursuing further education, such as a master's or Ph.D., can open doors to higher-level positions and salary brackets.
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Leadership Roles: Aim for leadership or managerial roles within your team or organization. This not only increases your salary but also enhances your career trajectory.
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Networking: Build a strong professional network. Engaging with industry peers can lead to new opportunities and insights into higher-paying roles.
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Performance and Negotiation: Consistently demonstrate high performance and be prepared to negotiate your salary during performance reviews or when taking on additional responsibilities.
Educational Requirements
Most Mid-level Decision Scientist roles require at least a bachelor's degree in a relevant field such as Computer Science, Statistics, Mathematics, or Engineering. However, a master's degree is often preferred and can be a significant advantage. Degrees in Data Science or Business Analytics are also highly regarded. A strong foundation in quantitative and analytical skills is essential, and coursework in machine learning, data mining, and statistical analysis is beneficial.
Helpful Certificates
While not always mandatory, certain certifications can enhance your profile and demonstrate your expertise:
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Certified Analytics Professional (CAP): Validates your ability to transform data into valuable insights.
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Google Professional Data Engineer: Demonstrates proficiency in designing, building, and operationalizing data processing systems.
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AWS Certified Machine Learning – Specialty: Shows expertise in building, training, tuning, and deploying machine learning models on AWS.
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Microsoft Certified: Azure Data Scientist Associate: Validates your skills in applying data science and machine learning to implement and run machine learning workloads on Azure.
Required Experience
Typically, a Mid-level Decision Scientist is expected to have 3-5 years of relevant experience. This experience should include hands-on work with data analysis, statistical modeling, and machine learning projects. Experience in a specific industry can also be beneficial, as it provides domain knowledge that can be crucial for decision-making roles. Additionally, experience with data visualization tools and programming languages such as Python or R is often required.
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