How Are Tech Salaries Shaping Up in 2020-2024? Part 2
by John Mike Asuncion
Executive Summary
This Level 2 analysis examines tech salary trends from 2020 to 2024 using a dataset of 57,194 salary records. The report addresses three key objectives to provide actionable insights for an international HR consultancy:
- Top 5 Job Titles by Salary: The highest-paying roles are Analytics Engineering Manager ($399,880), Data Science Tech Lead ($375,000), Applied AI/ML Lead ($292,500), Head of Machine Learning ($288,701), and Engineering Manager ($262,526), as visualized in a bar chart.
- Remote Work Salary Trends: On-site workers (0%) earn the highest average salary at $162,402, followed by fully remote (100%) at $149,675, and hybrid (50%) at $81,867, indicating a surprising premium for on-site work.
- Salary Distribution by Company Size: Medium companies (M) offer the highest average salary at $159,725, followed by large companies (L) at $152,617, and small companies (S) at $87,672, with a box plot illustrating the distribution.
Brief Recommendations: Offer competitive salaries above $375,000 for top roles, consider on-site work incentives given the salary premium, and benchmark against medium-sized companies. Enhance future data collection with variables like industry sector for deeper insights.
I. Background
Tech companies face fierce competition for talent, making salary insights critical for attracting and retaining skilled professionals. This analysis leverages a global salary dataset to uncover trends in job roles, experience levels, and remote work, helping an international HR consultancy stay competitive. With remote work surging and tech roles diversifying, understanding these drivers is more vital than ever.
II. Objectives
This report aims to provide actionable insights into tech salary trends by addressing the following goals:
- Create a bar chart displaying the top 5 job titles with the highest average salary (in USD).
- Compare the average salaries for employees working remotely 100%, 50%, and 0%, and identify patterns or trends.
- Visualize the salary distribution (in USD) across company sizes (S, M, L) and determine which company size offers the highest average salary.
III. Data Description
The dataset, sourced from a survey hosted by an HR consultancy, is stored in salaries.csv. Each row represents an employee’s salary record for a given year. The columns are as follows:
| Column Name | Description | Expected Data Type |
|---|---|---|
work_year | Year of work | int |
experience_level | Level of experience (e.g., EN, MI, SE, EX) | str |
employment_type | Type of employment (e.g., FT, PT, CT, FL) | str |
job_title | Job title of the employee | str |
salary | Salary amount in original currency | int |
salary_currency | Currency of the salary | str |
salary_in_usd | Salary amount converted to USD | int |
employee_residence | Country code of employee residence | str |
remote_ratio | Remote work ratio (0, 50, 100) | int |
company_location | Country code of company location | str |
company_size | Size of the company (S, M, L) | str |
Let's load the data to begin our analysis.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
salaries_df = pd.read_csv('salaries.csv')
salaries_df.head()salaries_df.info()IV. Data Preparation
Data Quality Check and Data Cleaning
Data preparation ensures reliable analysis by addressing potential issues. Each check builds a foundation for trustworthy insights.
- Checking Column Headers
Headers guide our analysis. Missing or misnamed columns, like salary_in_usd, could derail salary comparisons.
salaries_df.columns.tolist()- Checking for Missing Values
Missing data, such as blank salaries, skews results. Checking ensures completeness for accurate averages.
salaries_df.isnull().sum()- Checking Data Types
Incorrect types, like text for salary_in_usd, prevent calculations. Verification ensures compatibility.