Business Intelligence Engineer vs. Applied Scientist

A Comprehensive Comparison between Business Intelligence Engineer and Applied Scientist Roles

5 min read · Dec. 6, 2023

Career

Business Intelligence Engineer vs. Applied Scientist

Definition
Responsibilities
Required Skills
Educational Background
Tools and Software Used
Common Industries
Outlook
Practical Tips for Getting Started
Conclusion

In the world of data science, Business Intelligence Engineer and Applied Scientist are two prominent roles that require a unique set of skills and expertise. While both roles involve working with data, they differ in 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 take a deep dive into the differences between these two roles.

Definition

A Business Intelligence Engineer is responsible for designing and maintaining the infrastructure that enables businesses to analyze and interpret data. They are responsible for collecting, analyzing, and interpreting large volumes of data to provide insights that can help businesses make informed decisions. They work closely with business stakeholders to understand their data needs and develop solutions that meet those needs.

On the other hand, an Applied Scientist is responsible for developing and implementing Machine Learning models to solve complex business problems. They use statistical analysis and machine learning algorithms to extract insights from data and develop predictive models that can help businesses make data-driven decisions. They work closely with data scientists, engineers, and business stakeholders to identify business problems and develop solutions that meet those needs.

Responsibilities

The responsibilities of a Business Intelligence Engineer include:

Designing and implementing data warehouses and Data pipelines
Developing and maintaining data models and ETL processes
Creating dashboards and reports to visualize data and provide insights
Collaborating with business stakeholders to understand their data needs and develop solutions that meet those needs
Ensuring data accuracy, consistency, and integrity
Identifying and resolving Data quality issues
Managing data Security and access control

The responsibilities of an Applied Scientist include:

Identifying business problems that can be solved using Machine Learning models
Collecting and preprocessing data for machine learning models
Developing and Testing machine learning models
Evaluating the performance of machine learning models and making improvements as necessary
Communicating insights and recommendations to business stakeholders
Collaborating with data scientists, engineers, and business stakeholders to develop solutions that meet business needs

Required Skills

The required skills for a Business Intelligence Engineer include:

Strong SQL skills
Proficiency in data modeling and ETL processes
Experience with Data visualization tools such as Tableau or Power BI
Knowledge of Data Warehousing concepts and technologies
Ability to collaborate effectively with business stakeholders
Strong problem-solving and analytical skills
Attention to detail and accuracy

The required skills for an Applied Scientist include:

Strong programming skills in Python or R
Knowledge of machine learning algorithms and statistical analysis
Experience with machine learning frameworks such as TensorFlow or PyTorch
Proficiency in data preprocessing and feature Engineering
Ability to evaluate and interpret machine learning model performance
Strong communication and presentation skills
Ability to collaborate effectively with data scientists, engineers, and business stakeholders

Educational Background

The educational background required for a Business Intelligence Engineer typically includes a degree in Computer Science, information technology, or a related field. However, some employers may also consider candidates with degrees in business administration or Finance if they have relevant experience in Data analysis and modeling.

The educational background required for an Applied Scientist typically includes a degree in computer science, Statistics, Mathematics, or a related field. A master's or Ph.D. degree in a related field is preferred for this role.

Tools and Software Used

The tools and software used by a Business Intelligence Engineer include:

SQL and NoSQL databases such as MySQL, Oracle, or MongoDB
Data warehousing technologies such as Redshift, Snowflake, or BigQuery
ETL tools such as Informatica, Talend, or Apache NiFi
Data visualization tools such as Tableau, Power BI, or QlikView
Cloud platforms such as AWS, Azure, or Google Cloud

The tools and software used by an Applied Scientist include:

Programming languages such as Python or R
Machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn
Data preprocessing tools such as Pandas, NumPy, or SciPy
Cloud platforms such as AWS, Azure, or Google Cloud

Common Industries

Business Intelligence Engineers are in high demand in industries such as finance, healthcare, retail, and E-commerce. They are also sought after by Consulting firms and technology companies that provide Data Analytics solutions to businesses.

Applied Scientists are in high demand in industries such as finance, healthcare, retail, E-commerce, and telecommunications. They are also sought after by technology companies that develop machine learning solutions for businesses.

Outlook

The job outlook for both roles is very promising. According to the Bureau of Labor Statistics, the employment of computer and information technology occupations, which includes Business Intelligence Engineers, is projected to grow 11 percent from 2019 to 2029, much faster than the average for all occupations. The 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.