Data Modeller vs. Machine Learning Research Engineer

Data Modeller vs Machine Learning Research Engineer: Which Career Path is Right for You?

Table of contents

As the field of artificial intelligence and Big Data continues to grow, the demand for professionals in related roles is also increasing. Two popular career paths in this space are Data Modeller and Machine Learning Research Engineer. While both roles involve working with data and employing technical skills, they differ in terms of their 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 each of these aspects in detail to help you determine which career path is right for you.

Definitions

First, let's define each of these roles:

A Data Modeller is responsible for designing, implementing, and maintaining data models that support business processes. They work closely with stakeholders to understand their requirements and develop data models that capture the data needed to support those requirements. Data Modellers are also responsible for ensuring Data quality, managing metadata, and documenting data models.

A Machine Learning Research Engineer, on the other hand, is responsible for developing and implementing machine learning algorithms that can be used to solve business problems. They work with large datasets, develop models, and use statistical analysis to make predictions or identify patterns. Machine Learning Research Engineers are also responsible for evaluating the performance of their models and making improvements to increase accuracy.

Responsibilities

The responsibilities of Data Modellers and Machine Learning Research Engineers differ significantly:

Data Modeller Responsibilities

• Designing and implementing data models
• Ensuring data quality
• Managing metadata
• Documenting data models
• Collaborating with stakeholders to understand business requirements
• Developing and maintaining data dictionaries

Machine Learning Research Engineer Responsibilities

• Developing and implementing machine learning algorithms
• Working with large datasets
• Developing models and using statistical analysis to make predictions or identify patterns
• Evaluating the performance of models
• Making improvements to increase accuracy
• Collaborating with stakeholders to understand business requirements

Required Skills

Both Data Modellers and Machine Learning Research Engineers require a broad range of technical skills, but the specific skills required for each role differ:

Data Modeller Required Skills

• Strong understanding of data modelling concepts
• Proficiency in SQL and other database technologies
• Experience with data modelling tools and software
• Attention to detail
• Strong communication skills
• Ability to collaborate with stakeholders

Machine Learning Research Engineer Required Skills

• Strong understanding of machine learning concepts
• Proficiency in at least one programming language (such as Python or R)
• Experience with machine learning tools and software (such as TensorFlow or Scikit-learn)
• Strong mathematical skills
• Attention to detail
• Strong analytical skills

Educational Backgrounds

To become a Data Modeller or Machine Learning Research Engineer, you will need a strong educational background in Computer Science or a related field:

Data Modeller Educational Background

• Bachelor's degree in computer science, information technology, or a related field
• Certification in data modelling or database technologies (optional)

Machine Learning Research Engineer Educational Background

• Bachelor's degree in computer science, Mathematics, statistics, or a related field
• Master's or PhD in computer science, mathematics, statistics, or a related field (preferred)

Tools and Software Used

Both Data Modellers and Machine Learning Research Engineers use a variety of tools and software to perform their jobs:

Data Modeller Tools and Software

• ER/Studio
• ERwin
• MySQL Workbench
• Oracle SQL Developer Data Modeler

Machine Learning Research Engineer Tools and Software

• TensorFlow
• scikit-learn
• PyTorch
• Keras

Common Industries

Data Modellers and Machine Learning Research Engineers work in a variety of industries, including:

Data Modeller Common Industries

• Banking and finance
• Healthcare
• Retail
• Telecommunications
• Government

Machine Learning Research Engineer Common Industries

• Technology
• Healthcare
• Finance
• Retail
• Gaming

Outlook

Both Data Modellers and Machine Learning Research Engineers have a positive job outlook:

Data Modeller Job Outlook

• The US Bureau of Labor Statistics (BLS) projects a 9% growth rate for database administrators, which includes Data Modellers, from 2018-2028.

Machine Learning Research Engineer Job Outlook

• The BLS projects a 11% growth rate for computer and information research scientists, which includes Machine Learning Research Engineers, from 2018-2028.

Practical Tips for Getting Started

If you are interested in pursuing a career as a Data Modeller or Machine Learning Research Engineer, here are some practical tips to get started:

Practical Tips for Getting Started as a Data Modeller

• Learn SQL and other database technologies
• Gain experience with data modelling tools and software
• Develop strong communication skills
• Consider certification in data modelling or database technologies

Practical Tips for Getting Started as a Machine Learning Research Engineer

• Learn at least one programming language (such as Python or R)
• Gain experience with machine learning tools and software (such as TensorFlow or scikit-learn)
• Develop strong mathematical and analytical skills
• Consider pursuing a Master's or PhD in computer science, mathematics, statistics, or a related field

Conclusion

Both Data Modellers and Machine Learning Research Engineers play important roles in the field of artificial intelligence and big data. While their responsibilities, required skills, educational backgrounds, tools and software used, common industries, and outlooks differ, both offer promising career paths for those with a strong technical background and an interest in data and analytics. By considering the information presented here, you can determine which career path is right for you and take practical steps to pursue that path.