Applied Scientist vs. Head of Data Science

Comparison between Applied Scientist and Head of Data Science Roles

Table of contents

In the field of AI/ML and Big Data, two roles that are often discussed are Applied Scientist and Head of Data Science. While both roles are related to data science, they have different responsibilities, required skills, educational backgrounds, tools and software used, common industries, outlooks, and practical tips for getting started in these careers. In this article, we will explore the differences between these two roles.

Definitions

An Applied Scientist is a professional who uses scientific methods to solve practical problems. In the context of AI/ML and Big Data, Applied Scientists use their knowledge of Machine Learning, Statistics, and Computer Science to develop algorithms and models that can be applied to real-world problems. They work on specific projects and are responsible for designing, implementing, and Testing solutions.

A Head of Data Science, on the other hand, is a senior executive who oversees the data science team in an organization. They are responsible for developing and implementing the data science strategy for the organization, managing the data science team, and communicating with other executives about the impact of data science on the organization's goals.

Responsibilities

The responsibilities of an Applied Scientist include:

• Designing and developing Machine Learning models and algorithms
• Testing and validating models
• Analyzing data to identify trends and patterns
• Collaborating with other professionals, such as software developers and data engineers, to integrate models into software applications
• Staying up-to-date with the latest Research and developments in the field of AI/ML and Big Data

The responsibilities of a Head of Data Science include:

• Developing and implementing the data science strategy for the organization
• Managing the data science team
• Communicating with other executives about the impact of data science on the organization's goals
• Ensuring that the data science team is aligned with the organization's goals and priorities
• Staying up-to-date with the latest research and developments in the field of AI/ML and Big Data

Required Skills

The required skills for an Applied Scientist include:

• Knowledge of machine learning algorithms and models
• Proficiency in programming languages such as Python, R, and Java
• Familiarity with Data analysis and visualization tools such as Tableau and Power BI
• Ability to work with large datasets
• Strong problem-solving skills
• Strong communication skills

The required skills for a Head of Data Science include:

• Leadership skills
• Strategic thinking
• Strong communication skills
• Knowledge of business operations and strategy
• Knowledge of data science tools and techniques
• Ability to manage a team

Educational Background

An Applied Scientist typically holds a master's or doctoral degree in a field related to AI/ML and Big Data, such as computer science, statistics, or Mathematics. They may also have experience working in a research environment.

A Head of Data Science typically holds a master's or doctoral degree in a field related to AI/ML and Big Data, as well as experience in a leadership role. They may also have experience in business operations or strategy.

Tools and Software Used

The tools and software used by an Applied Scientist include:

• Programming languages such as Python, R, and Java
• Machine learning frameworks such as TensorFlow and PyTorch
• Data analysis and visualization tools such as Tableau and Power BI
• Cloud computing platforms such as AWS and Azure

The tools and software used by a Head of Data Science include:

• Project management tools such as Jira and Trello
• Data visualization tools such as Tableau and Power BI
• Business Intelligence tools such as SAP and Oracle
• Cloud computing platforms such as AWS and Azure

Common Industries

Applied Scientists are employed in a variety of industries, including:

• Technology
• Healthcare
• Finance
• Retail
• Manufacturing

Head of Data Science roles are typically found in larger organizations, such as:

• Technology companies
• Financial institutions
• Consulting firms
• Healthcare organizations
• Retail companies

Outlooks

The outlook for both Applied Scientists and Heads of Data Science is positive. The demand for professionals with skills in AI/ML and Big Data is growing rapidly, and this trend is expected to continue in the coming years.

According to the Bureau of Labor Statistics, employment of computer and information Research scientists, which includes Applied Scientists, is projected to grow 15 percent from 2019 to 2029, much faster than the average for all occupations.

Similarly, the demand for Heads of Data Science is also expected to grow in the coming years, as more organizations recognize the value of data science in achieving their goals.

Practical Tips for Getting Started

If you are interested in pursuing a career as an Applied Scientist, here are some practical tips:

• Obtain a master's or doctoral degree in a field related to AI/ML and Big Data.
• Gain experience working in a research environment.
• Learn programming languages such as Python, R, and Java.
• Familiarize yourself with machine learning frameworks such as TensorFlow and PyTorch.
• Develop strong problem-solving and communication skills.

If you are interested in pursuing a career as a Head of Data Science, here are some practical tips:

• Obtain a master's or doctoral degree in a field related to AI/ML and Big Data.
• Gain experience in a leadership role.
• Develop strong communication and strategic thinking skills.
• Familiarize yourself with business operations and strategy.
• Learn project management tools such as Jira and Trello.

In conclusion, while both Applied Scientists and Heads of Data Science are related to the field of AI/ML and Big Data, they have different responsibilities, required skills, educational backgrounds, tools and software used, common industries, outlooks, and practical tips for getting started in these careers. By understanding these differences, you can make an informed decision about which role is right for you.