Research Scientist vs. Data Modeller

Research Scientist vs Data Modeller: A Comprehensive Comparison

Table of contents

If you are considering a career in the AI/ML and Big Data space, you may have come across the roles of Research Scientist and Data Modeller. While both roles involve working with data, they have different responsibilities and skill sets. In this article, we will compare these two roles in detail, covering their definitions, responsibilities, required skills, educational backgrounds, tools and software used, common industries, outlooks, and practical tips for getting started in these careers.

Definitions

A Research Scientist is a professional who conducts research and experiments to develop new technologies or improve existing ones. They work in a variety of industries, including academia, government, and private companies. Research Scientists in the AI/ML and Big Data space are responsible for designing and implementing algorithms and models to analyze large datasets and extract insights.

A Data Modeller, on the other hand, is a professional who creates data models to represent information and relationships between data elements. They work in industries such as Finance, healthcare, and retail, where they help organizations make informed decisions based on data. Data Modellers design, implement, and maintain data models to ensure data accuracy and consistency.

Responsibilities

The responsibilities of a Research Scientist in the AI/ML and Big Data space include:

• Designing and implementing algorithms and models to analyze large datasets
• Developing new techniques for Data analysis and visualization
• Conducting experiments to validate hypotheses and test new technologies
• Collaborating with other researchers and engineers to develop new products and services
• Staying up-to-date with the latest research and trends in the field

The responsibilities of a Data Modeller include:

• Creating and maintaining data models to ensure data accuracy and consistency
• Identifying data sources and developing data integration strategies
• Collaborating with business analysts and data scientists to understand data requirements
• Developing data dictionaries and metadata repositories
• Ensuring data models comply with industry standards and regulations

Required Skills

To be a successful Research Scientist in the AI/ML and Big Data space, you need to have the following skills:

• Strong mathematical and statistical skills
• Proficiency in programming languages such as Python, R, and Java
• Knowledge of Machine Learning algorithms and techniques
• Experience with Data visualization tools such as Tableau and Power BI
• Strong problem-solving and analytical skills
• Good communication and collaboration skills

To be a successful Data Modeller, you need to have the following skills:

• Strong knowledge of data modeling techniques and methodologies
• Proficiency in data modeling tools such as ERwin, ER/Studio, and Visio
• Knowledge of database management systems such as SQL Server and Oracle
• Experience with data integration and ETL tools such as Informatica and Talend
• Strong problem-solving and analytical skills
• Good communication and collaboration skills

Educational Background

To become a Research Scientist in the AI/ML and Big Data space, you typically need to have a Ph.D. in Computer Science, statistics, or a related field. However, some companies may hire candidates with a master's degree or a bachelor's degree in a related field and relevant work experience.

To become a Data Modeller, you typically need to have a bachelor's degree in computer science, information technology, or a related field. However, some companies may hire candidates with a diploma or certificate in data modeling and relevant work experience.

Tools and Software Used

Research Scientists in the AI/ML and Big Data space use a variety of tools and software, including:

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

Data Modellers use a variety of tools and software, including:

• Data modeling tools such as ERwin, ER/Studio, and Visio
• Database management systems such as SQL Server and Oracle
• Data integration and ETL tools such as Informatica and Talend
• Business Intelligence tools such as SAP BusinessObjects and IBM Cognos

Common Industries

Research Scientists in the AI/ML and Big Data space work in a variety of industries, including:

• Technology companies such as Google, Microsoft, and Amazon
• Healthcare companies such as Pfizer and Johnson & Johnson
• Financial services companies such as JPMorgan Chase and Goldman Sachs
• Government agencies such as NASA and the National Institutes of Health

Data Modellers work in a variety of industries, including:

• Financial services companies such as banks and insurance companies
• Healthcare companies such as hospitals and clinics
• Retail companies such as Walmart and Target
• Government agencies such as the Internal Revenue Service and the Census Bureau

Outlooks

According to the Bureau of Labor Statistics, the employment of computer and information research scientists is projected to grow 15 percent from 2019 to 2029, much faster than the average for all occupations. The demand for Research Scientists in the AI/ML and Big Data space is expected to remain strong due to the increasing adoption of these technologies in various industries.

The employment of computer and information systems managers, which includes Data Modellers, is projected to grow 10 percent from 2019 to 2029, much faster than the average for all occupations. The demand for Data Modellers is expected to remain strong due to the increasing importance of data-driven decision making in various industries.

Practical Tips for Getting Started

If you are interested in becoming a Research Scientist in the AI/ML and Big Data space, here are some practical tips for getting started:

• Pursue a Ph.D. in computer science, statistics, or a related field
• Participate in research projects and internships to gain practical experience
• Develop a strong portfolio of projects and publications
• Stay up-to-date with the latest research and trends in the field

If you are interested in becoming a Data Modeller, here are some practical tips for getting started:

• Pursue a bachelor's degree in computer science, information technology, or a related field
• Gain experience in database management and data modeling through internships or entry-level positions
• Develop a strong knowledge of data modeling techniques and methodologies
• Stay up-to-date with industry standards and regulations

Conclusion

In conclusion, Research Scientists and Data Modellers both play important roles in the AI/ML and Big Data space, but they have different responsibilities and skill sets. Research Scientists are responsible for developing new technologies and algorithms to analyze large datasets, while Data Modellers are responsible for creating and maintaining data models to ensure data accuracy and consistency. Regardless of which career path you choose, both roles offer exciting opportunities for growth and innovation in the field of data science.