Data608_story4_YR

Story 4 - How much do we get paid?

I have introduced the term “Data Practitioner” as a generic job descriptor because we have so many different job role titles for individuals whose work activities overlap including Data Scientist, Data Engineer, Data Analyst, Business Analyst, Data Architect, etc.

For this story we will answer the question, “How much do we get paid?” Your analysis and data visualizations must address the variation in average salary based on role descriptor and state.

This analysis examines compensation patterns for data practitioners in the United States, exploring how salaries vary across roles, experience levels, time periods, and company sizes. Using 2,800 salary records from the Kaggle Data Science Salaries dataset (2020-2024), I analyzed five key data roles: Machine Learning Engineer, Data Architect, Data Scientist, Data Engineer, and Business Intelligence Analyst

Link to the dataset: https://www.kaggle.com/datasets/sazidthe1/data-science-salaries

Role selection significantly impacts earning potential, with specialized technical roles (ML, Architecture) commanding higher compensation. Data Engineers and Data Scientists cluster closely ($150K-$165K median), suggesting these roles are viewed similarly by employers despite different skill focuses

2024 marks a turning point, with most roles showing flat or slightly declining salaries compared to 2023, potentially reflecting broader tech industry corrections, increased talent supply from bootcamps and training programs, and market maturation following pandemic-era rapid growth.

The salaries nearly doubling from Entry-level to Executive-level across all five roles. Entry-level positions cluster between $85K-$118K, with Machine Learning Engineers having a higher salary even at career start ($118K).

While medium and large company categories have robust sample sizes (n=2,641 and n=137 respectively), small company data is extremely limited (n=22), making those findings not as reliable.