Applied Scientist vs. Decision Scientist

Applied Scientist vs Decision Scientist: A Comprehensive Comparison

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The fields of Artificial Intelligence (AI), Machine Learning (ML), and Big Data have been growing rapidly in recent years, leading to an increase in demand for specialized professionals in these areas. Two such roles that have gained popularity are Applied Scientist and Decision Scientist. While both roles are related to Data analysis, they differ in their focus and responsibilities. In this article, we will compare and contrast these roles in terms of their definitions, responsibilities, required skills, educational backgrounds, tools and software used, common industries, outlooks, and practical tips for getting started in these careers.

Definitions

An Applied Scientist is a professional who applies scientific methods, algorithms, and Data analysis techniques to solve real-world problems in various domains. They are responsible for designing, implementing, and evaluating machine learning models, as well as conducting experiments to validate their efficacy. Applied Scientists work closely with software engineers, data engineers, and product managers to develop and deploy AI/ML solutions.

A Decision Scientist, on the other hand, is a professional who uses data and analytics to support business decision-making. They work with stakeholders to identify strategic business questions and use statistical models and algorithms to provide insights and recommendations. Decision Scientists are responsible for designing and executing experiments, analyzing data, and communicating their findings to stakeholders.

Responsibilities

The responsibilities of Applied Scientists and Decision Scientists differ in terms of their focus and scope.

Applied Scientist Responsibilities

• Conduct Research and experiments to develop and improve machine learning models
• Design and implement algorithms and Data pipelines
• Optimize model performance and scalability
• Collaborate with cross-functional teams to develop and deploy AI/ML solutions
• Stay up-to-date with the latest Research and industry trends

Decision Scientist Responsibilities

• Collaborate with stakeholders to identify strategic business questions
• Design and execute experiments to answer these questions
• Analyze data using statistical models and algorithms
• Communicate insights and recommendations to stakeholders
• Develop and maintain dashboards and reports to track key performance indicators

Required Skills

Both Applied Scientists and Decision Scientists require a combination of technical and soft skills.

Applied Scientist Skills

• Strong foundation in Mathematics, Statistics, and Computer Science
• Proficiency in programming languages (Python, R, Java, etc.) and data analysis tools (Pandas, NumPy, Scikit-learn, etc.)
• Experience with machine learning algorithms and frameworks (TensorFlow, PyTorch, Keras, etc.)
• Knowledge of data structures, algorithms, and software Engineering principles
• Excellent problem-solving and analytical skills
• Strong communication and collaboration skills

Decision Scientist Skills

• Strong foundation in Mathematics, statistics, and data analysis
• Proficiency in statistical programming languages (R, Python, SAS, etc.) and data analysis tools (Excel, Tableau, Power BI, etc.)
• Experience with Statistical modeling and experimental design
• Knowledge of data structures, algorithms, and database systems
• Excellent problem-solving and analytical skills
• Strong communication and collaboration skills

Educational Backgrounds

Applied Scientists and Decision Scientists have different educational backgrounds.

Applied Scientist Educational Backgrounds

• Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or a related field
• PhD in Computer Science, Mathematics, Statistics, or a related field (optional)

Decision Scientist Educational Backgrounds

• Bachelor's or Master's degree in Mathematics, Statistics, Economics, or a related field
• MBA or Master's degree in Business Administration (optional)

Tools and Software Used

Applied Scientists and Decision Scientists use different tools and software in their work.

Applied Scientist Tools and Software

• Programming languages: Python, R, Java, C++, etc.
• Data analysis tools: Pandas, NumPy, Scikit-learn, etc.
• Machine learning frameworks: TensorFlow, PyTorch, Keras, etc.
• Cloud platforms: AWS, Azure, Google Cloud, etc.
• Data visualization tools: Matplotlib, Seaborn, Plotly, etc.

Decision Scientist Tools and Software

• Statistical programming languages: R, Python, SAS, etc.
• Data analysis tools: Excel, Tableau, Power BI, etc.
• Database systems: SQL, NoSQL, etc.
• Business Intelligence tools: SAP, Oracle, etc.
• Data visualization tools: Tableau, Power BI, etc.

Common Industries

Applied Scientists and Decision Scientists work in different industries.

Applied Scientist Industries

• Technology companies (Google, Microsoft, Amazon, etc.)
• Healthcare companies (IBM Watson Health, Philips, etc.)
• Financial services companies (JP Morgan Chase, Goldman Sachs, etc.)
• Retail and E-commerce companies (Amazon, Walmart, etc.)
• Gaming and entertainment companies (Activision Blizzard, Electronic Arts, etc.)

Decision Scientist Industries

• Consulting firms (McKinsey, Bain, BCG, etc.)
• Financial services companies (Goldman Sachs, Morgan Stanley, etc.)
• Healthcare companies (UnitedHealth Group, Aetna, etc.)
• Retail and E-commerce companies (Amazon, Walmart, etc.)
• Technology companies (Google, Microsoft, Amazon, etc.)

Outlooks

Both Applied Scientists and Decision Scientists have positive outlooks in terms of job growth and salary potential.

Applied Scientist Outlook

• According to the Bureau of Labor Statistics, the employment of computer and information research scientists (which includes Applied Scientists) is projected to grow 15% from 2019 to 2029, much faster than the average for all occupations.
• The median annual wage for computer and information research scientists was $126,830 in May 2020.

Decision Scientist Outlook

• According to Glassdoor, the average base salary for a Decision Scientist is $113,309 per year in the United States.
• The employment of Management Analysts (which includes Decision Scientists) is projected to grow 11% from 2019 to 2029, much faster than the average for all occupations.

Practical Tips for Getting Started

If you are interested in pursuing a career as an Applied Scientist or Decision Scientist, here are some practical tips to get started.

Applied Scientist Tips

• Build a strong foundation in mathematics, statistics, and computer science.
• Learn programming languages (Python, R, Java, etc.) and data analysis tools (Pandas, Numpy, Scikit-learn, etc.).
• Gain experience with Machine Learning algorithms and frameworks (TensorFlow, PyTorch, Keras, etc.).
• Participate in online courses, hackathons, and competitions to build your portfolio.
• Network with professionals in the industry and attend conferences and meetups.

Decision Scientist Tips

• Build a strong foundation in mathematics, statistics, and data analysis.
• Learn statistical programming languages (R, Python, SAS, etc.) and data analysis tools (Excel, Tableau, Power BI, etc.).
• Gain experience with Statistical modeling and experimental design.
• Participate in case competitions and Consulting projects to build your portfolio.
• Network with professionals in the industry and attend conferences and meetups.

Conclusion

Applied Scientists and Decision Scientists are two distinct roles in the AI/ML and Big Data space, with different responsibilities, required skills, educational backgrounds, tools and software used, common industries, outlooks, and practical tips for getting started. While both roles require a strong foundation in mathematics, statistics, and data analysis, Applied Scientists focus on developing and implementing machine learning models, while Decision Scientists focus on using data and analytics to support business decision-making. Regardless of which role you choose, both offer exciting and rewarding career opportunities in a growing field.