Salary for Mid-level / Intermediate Decision Scientist in United States during 2023

Salary details

The average mid-level / intermediate Decision Scientist salary lies between USD 142,200 and USD 204,500 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: Nov. 24, 2024

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, Machine Learning and SQL. Below you find a list of the 20 most occuring job tags in 2023 and the number of open jobs that where associated with them during that period:

Python | 2 jobs Machine Learning | 2 jobs SQL | 2 jobs Engineering | 2 jobs Banking | 2 jobs Statistics | 2 jobs R | 1 jobs Deep Learning | 1 jobs ANN | 1 jobs Economics | 1 jobs Consulting | 1 jobs Data pipelines | 1 jobs Testing | 1 jobs FinTech | 1 jobs Business Analytics | 1 jobs Unstructured data | 1 jobs ML models | 1 jobs Pipelines | 1 jobs Credit risk | 1 jobs Mathematics | 1 jobs

Top 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 Health care, Career development and Insurance. Below you find a list of the 20 most occuring job perks or benefits in 2023 and the number of open jobs that where offering them during that period:

Health care | 2 jobs Career development | 2 jobs Insurance | 2 jobs Salary bonus | 1 jobs

Salary Composition

The salary for a Mid-level/Intermediate 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 is the fixed component and usually constitutes the majority of the total compensation package. Performance bonuses can vary significantly depending on the company's profitability and individual performance metrics. Additional remuneration might include stock options, especially in tech companies, and benefits like health insurance, retirement plans, and paid time off. The composition can vary by region, with tech hubs like San Francisco and New York offering higher base salaries but potentially lower relative bonuses due to the high cost of living. Industry also plays a role; for instance, finance and tech sectors often provide more lucrative bonuses compared to academia or non-profits. Company size can influence stock options and bonuses, with larger companies typically offering more comprehensive packages.

Steps to Increase Salary

To increase your salary from this position, consider pursuing advanced roles such as Senior Decision Scientist or Data Science Manager. This often requires demonstrating leadership skills, taking on more complex projects, and showing a track record of impactful decision-making. Networking within your industry can open doors to higher-paying opportunities. Additionally, specializing in high-demand areas like machine learning engineering, AI ethics, or big data analytics can make you more valuable. Pursuing further education, such as a master's degree or MBA, can also enhance your qualifications and salary prospects. Negotiating your salary during performance reviews or when taking on new responsibilities can also lead to increases.

Educational Requirements

Most Mid-level/Intermediate 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 particularly relevant. Some positions may also value interdisciplinary studies that combine technical skills with business acumen.

Helpful Certifications

Certifications can bolster your credentials and demonstrate expertise in specific areas. Common and helpful certifications include:

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

These certifications can validate your skills in data analysis, machine learning, and cloud computing, making you more competitive in the job market.

Required Experience

Typically, a Mid-level/Intermediate 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. Experience in a specific industry can be beneficial, as it provides context for decision-making processes. Demonstrated experience in leading projects or teams can also be advantageous.

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