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Data Scientist

What is a Data Scientist?

Data Scientists use computer science, statistics, and mathematics to apply machine learning, predictive modeling, and statistical analysis to extract insights and aid decision-making about complex digital data, such as website usage, sales, logistics and customer engagement. 

Salary Range

25th Percentile
$ 0
50th Percentile
$ 0
75th Percentile
$ 0

Foundational Skills

Adaptability, Analytical & Critical Thinking, Basic Computer Competencies, Communication Skills, Creative Thinking, Customer Focus, Dependability, Detail Orientation, Humility, Initiative, Instruction/Teaching Abilities, Integrity, Interpersonal Competencies, Leadership, Lifelong Learning, Mathematics, Problem Solving & Decision-Making, Professionalism, Reading, Science & Technology Understanding, Scheduling & Coordinating, Teamwork & Writing Skills

Occupational Skills

Data Analysis and Visualization

This competency requires proficiency in statistical analysis and the use of visualization tools to create understandable and actionable graphs and charts. It also includes the translation of data findings into clear visual stories that can inform business decisions.

Novice Level
  • Can perform basic descriptive and inferential statistical analysis. Can create basic visualizations of data

Emerging Level
  • Can perform more advanced statistical analysis and data mining techniques

  • Can create complex and informative visualizations of data

Proficient Level
  • Can apply a wide range of statistical and data mining techniques to solve complex problems

  • Can create highly effective and insightful visualizations of data

Data Modeling and Machine Learning

Design, implementation, and validation of predictive models that can learn from past data to forecast future trends, behaviors, and outcomes. It requires an understanding of machine learning algorithms and data modeling techniques, and the ability to apply these in various scenarios to support decision-making processes.

Novice Level
  • Can understand and apply basic machine learning algorithms

Emerging Level
  • Can build and train more complex machine learning models

Proficient Level
  • Can develop and deploy state-of-the-art machine learning models to solve real-world problems

Data Wrangling and Cleaning

Data wrangling and cleaning are about transforming and mapping data from its raw form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes. This process includes cleaning data to ensure accuracy, completeness, and consistency, which is crucial for reliable analysis.

Novice Level
  • Can clean and prepare data for analysis

Emerging Level
  • Can handle more complex data wrangling tasks, such as dealing with missing values and outliers

Proficient Level
  • Can automate data wrangling and cleaning processes using tools and techniques like data pipelines

Domain Expertise

Domain expertise refers to in-depth knowledge and understanding of the specific field or industry in which the data scientist operates. It involves grasping the nuances, trends, and key performance indicators of the domain, which is critical to applying data science techniques effectively and to ensuring that the insights generated are relevant and actionable within that context.

Novice Level
  • Has a basic understanding of the domain in which they are working

Emerging Level
  • Can apply their data science skills to solve specific problems in their domain

Proficient Level
  • Has deep expertise in their domain and can use data science to drive innovation and decision-making


Experience + Training

Career Resources
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