Salary for Mid-level / Intermediate Decision Scientist in United States during 2024
💰 The median Salary for Mid-level / Intermediate Decision Scientist in United States during 2024 is USD 133,950
✏️ This salary info is based on 40 individual salaries reported during 2024
Salary details
The average mid-level / intermediate Decision Scientist salary lies between USD 86,520 and USD 170,000 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.
- Job title
- Decision Scientist
- Experience
- Mid-level / Intermediate
- Region
- United States
- Salary year
- 2024
- Sample size
- 40
- 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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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: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 | 40 jobs SQL | 40 jobs Statistics | 31 jobs Engineering | 27 jobs Mathematics | 21 jobs Machine Learning | 20 jobs R | 18 jobs Tableau | 18 jobs Economics | 17 jobs Computer Science | 16 jobs Research | 13 jobs Testing | 13 jobs CX | 11 jobs Power BI | 10 jobs Data visualization | 10 jobs Data Analytics | 9 jobs Data analysis | 9 jobs Hadoop | 8 jobs Consulting | 8 jobs Excel | 8 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 | 35 jobs Equity / stock options | 29 jobs Health care | 22 jobs Competitive pay | 12 jobs Salary bonus | 11 jobs Insurance | 10 jobs Team events | 9 jobs Flex hours | 8 jobs Parental leave | 7 jobs Startup environment | 7 jobs Relocation support | 7 jobs Medical leave | 6 jobs Unlimited paid time off | 5 jobs 401(k) matching | 4 jobs Wellness | 4 jobs Flex vacation | 2 jobs Conferences | 2 jobs Snacks / Drinks | 2 jobs Flexible spending account | 2 jobs Flat hierarchy | 1 jobsSalary Composition for a Mid-level Decision Scientist
The salary for a Mid-level Decision Scientist in the United States 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 ranging from 70% to 85%. Performance bonuses can vary significantly depending on the company and industry, often making up 10% to 20% of the total compensation. Additional remuneration, such as stock options, profit-sharing, or other benefits, can account for 5% to 10%.
Regional differences can also impact salary composition. For instance, tech hubs like San Francisco or New York may offer higher base salaries and stock options due to the competitive market. In contrast, companies in smaller cities might offer a higher percentage of bonuses to attract talent. Industry-wise, tech and finance sectors tend to offer more lucrative packages compared to healthcare or education sectors. Larger companies often provide more comprehensive benefits and stock options, while smaller companies might focus on higher base salaries to attract talent.
Steps to Increase Salary from a Mid-level Position
To increase your salary from a Mid-level Decision Scientist position, consider the following strategies:
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Skill Enhancement: Continuously update your skills in emerging AI/ML technologies and tools. Specializing in niche areas like deep learning, natural language processing, or computer vision can make you more valuable.
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Advanced Education: Pursuing a master's degree or Ph.D. in a relevant field can open doors to higher-paying roles and leadership positions.
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Networking: Engage with professional networks and communities. Attending conferences, workshops, and meetups can lead to new opportunities and insights into higher-paying roles.
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Leadership Roles: Aim for roles that involve team leadership or project management, as these often come with higher compensation.
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Industry Switch: Consider moving to industries known for higher pay scales, such as finance or tech.
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 significantly enhance your prospects. Degrees in Data Science or Business Analytics are also highly regarded. A strong foundation in quantitative skills and programming is essential, and coursework in machine learning, data mining, and statistical analysis is beneficial.
Helpful Certifications
While not always mandatory, certain certifications can bolster your credentials and demonstrate expertise:
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Certified Analytics Professional (CAP): Validates your ability to transform data into valuable insights.
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Google Professional Machine Learning Engineer: Demonstrates proficiency in designing, building, and productionizing ML models.
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AWS Certified Machine Learning – Specialty: Shows expertise in using AWS services for machine learning.
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Microsoft Certified: Azure AI Engineer Associate: Highlights skills in using Azure AI services.
These certifications can enhance your resume and may lead to better job opportunities and salary negotiations.
Required Experience
Typically, a Mid-level Decision Scientist is expected to have 3 to 5 years of relevant experience. This experience should include hands-on work with data analysis, machine learning model development, and statistical modeling. Experience in a specific industry can also be advantageous, as it provides domain knowledge that can be critical for decision-making roles.
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