TLDR;

• Data Scientist
• Machine Learning Engineer
• Data Analyst
• AI Engineer / AI Specialist
• Backend Web Developer
• Full-Stack Developer
• Software Engineer
• DevOps Engineer
• QA Automation Engineer (SDET)
• Site Reliability Engineer (SRE)
• Python Game Developer
• Quantitative Analyst (“Quant”)
• Cybersecurity Analyst
  

So, you’re thinking about learning Python. Or maybe you’ve already started. You keep hearing it’s the language to learn, but you have one big question: What can you actually do with it? Let’s cut through the noise. Learning Python isn’t just an academic exercise; it’s a direct investment in a high-demand, high-paying career.

For each of the 13 roles below, we’ll show you what the job is, what it pays, and the single most important first step you can take to get there.

Want the quick overview? 👉 Skip ahead to the Python Jobs Salary Comparison Table.

13 Jobs Involving Python

1. Data Scientist

The rockstars of the data world! Data scientists use their skills in programming, statistics, and machine learning to extract meaningful insights from messy, complex data. They build predictive models to answer questions like “Which customers are most likely to cancel their subscription?”

• What they do: Clean and analyze data, build machine learning models, and communicate findings to business leaders.
• Average Salary: $125,000 – $175,000+ per year.
• Your First Step: Forget complex algorithms for a moment. Your first mission is to master the Pandas library. Find a messy dataset on Kaggle and use Pandas to clean it, handle missing values, and calculate basic statistics. This is the daily reality of the job.

2. Machine Learning Engineer

While a data scientist designs the model, the machine learning engineer builds and deploys it in a real-world production environment. They are expert software engineers who specialize in making machine learning work at scale, reliably and efficiently.

• What they do: Implement, test, and maintain machine learning models in production systems. They build the pipelines that feed data to the models and serve their predictions.
• Average Salary: $130,000 – $190,000+ per year.
• Your First Step: Pick one simple machine learning model, like Linear Regression. Don’t just use a library like Scikit-learn. Try to build it from scratch using only NumPy. This forces you to understand the math behind the magic, a critical skill for an ML engineer.

If you badly want to use Scikit-learn, this guide will definitely help you out!

3. Data Analyst

Data analyst is the first line of defense in making sense of data. They focus on gathering, cleaning, and visualizing data to identify trends and answer specific business questions. They are masters of SQL for data extraction and Python libraries like Matplotlib and Seaborn for visualization.

• What they do: Write database queries, create dashboards and reports, and present data-driven insights to help teams make better decisions.
• Average Salary: $75,000 – $110,000 per year.
• Your First Step: Download Google Analytics’ sample dataset. Your task is to use Python (with Pandas and Matplotlib) to create three charts that tell a story about the website’s users. For example: “Where do our best customers come from?” This is a perfect portfolio project.

4. AI Engineer / AI Specialist

This role is on the cutting edge, focused on building and integrating advanced AI systems, especially in areas like Natural Language Processing (NLP) and Computer Vision. They work with large language models (LLMs) and deep learning frameworks to create truly intelligent applications.

• What they do: Develop deep learning models, fine-tune existing models like GPT-4, and build APIs to make AI capabilities available to other software.
• Average Salary: $140,000 – $200,000+ per year.
• Your First Step: Use a pre-trained model from a library like Hugging Face Transformers. Write a simple Python script that uses a sentiment analysis model to determine if a movie review is positive or negative. This gets you hands-on with a state-of-the-art AI tool.

[PRO TIP: The lines between these data roles can be blurry.
Focus on the core skill: Data Scientists model, ML Engineers deploy, Data Analysts explain.]

5. Backend Web Developer

Every website or app has a “front-end” (what you see) and a “back-end” (the server, database, and application logic). A Python backend developer builds and maintains that server-side logic using powerful frameworks.

• What they do: Write server-side code, manage databases, and build APIs that the front-end talks to. They are the architects of the web’s hidden machinery.
• Average Salary: $110,000 – $150,000 per year.
• Your First Step: Choose one framework: Django or Flask.
  My advice? Start with Flask because it’s simpler. Your goal is to build a “To-Do List” application with two simple API endpoints: one to add a task and one to view all tasks.

6. Full-Stack Developer

A full-stack developer is a jack-of-all-trades who is comfortable working on both the front-end (HTML, CSS, JavaScript) and the back-end. With Python on the back-end and JavaScript on the front-end, you become an incredibly valuable asset who can build an entire application from scratch.

• What they do: Everything a backend developer does, plus building the user interface and user experience on the front-end.
• Average Salary: $120,000 – $165,000 per year.
• Your First Step: Take the Flask To-Do list you built. Now, create a simple HTML page with a form that can send a request to your “add task” endpoint. This is your first step from back-end to full-stack.

7. Software Engineer

This is a broader role, but many of the world’s top software engineers use Python as their primary tool. They design, build, and maintain software systems for desktops, servers, and large-scale services.

• What they do: Write clean, efficient, and testable code; design software architecture; and solve complex computational problems.
• Average Salary: $115,000 – $170,000+ per year.
• Your First Step: Learn about Object-Oriented Programming (OOP) in Python. Create a class for a Carwith attributes (like color, model) and methods (like start_engine(), drive()). This fundamental concept is the building block of almost all large software.

8. DevOps Engineer

DevOps engineers bridge the gap between software development (Dev) and IT operations (Ops). They use automation to build, test, and release software faster and more reliably. Python scripts are their secret weapon for automating repetitive tasks.

• What they do: Build CI/CD (Continuous Integration/Continuous Deployment) pipelines, manage cloud infrastructure (like AWS or Azure), and automate everything that can be automated.
• Average Salary: $120,000 – $160,000 per year.
• Your First Step: Use Python’s boto3 library to write a simple script that lists all the S3 buckets in an AWS account. This is a classic “Hello, World!” for cloud automation.

So, what does a DevOps Engineer exactly do?!

9. QA Automation Engineer

Also known as a Software Development Engineer in Test (SDET), this role involves writing code that tests other code. Instead of manually clicking through a website to find bugs, they write Python scripts using tools like Selenium or Pytest to automate the process.

• What they do: Build automated test suites, find bugs before users do, and ensure software quality and reliability.
• Average Salary: $95,000 – $130,000 per year.
• Your First Step: Install the Selenium library. Write a Python script that opens a web browser, navigates to https://www.google.com/search?q=google.com, enters a search query into the search bar, and clicks the search button. You’ve just written your first automated UI test.

10. Site Reliability Engineer (SRE)

Pioneered by Google, an SRE is a software engineer who focuses on the reliability, scalability, and performance of large systems. They have a “measure everything” mindset and write code to automate operations and prevent outages.

• What they do: Monitor system performance, respond to production incidents, and build software to improve system reliability.
• Average Salary: $135,000 – $185,000+ per year.
• Your First Step: Learn to use the requests and time libraries in Python. Write a script that pings a website every 10 seconds and prints out the response time. This is the foundation of performance monitoring.
  

11. Python Game Developer

While C++ dominates AAA games, Python is hugely popular for indie games and rapid prototyping. Libraries like Pygame provide a simple way to build 2D games, making it a great entry point into game development logic.

• What they do: Design game mechanics, write game logic, and use Python libraries to create interactive entertainment.
• Average Salary: $80,000 – $120,000 per year.
• Your First Step: Install Pygame. Follow a tutorial to build a classic game like “Snake” or “Pong.” The goal isn’t a masterpiece; it’s to understand the game loop (input, update, render).
  

12. Quantitative Analyst (“Quant”)

In the high-stakes world of finance, Quants use advanced mathematical and statistical methods to develop and execute trading strategies. Python, with its powerful data analysis libraries, has become the go-to language for financial modeling and algorithmic trading.

• What they do: Analyze financial market data, build predictive trading models, and manage risk.
• Average Salary: $150,000 – $250,000+ (can be much higher with bonuses).
• Your First Step: Use the yfinance library to download historical stock price data for a company like Apple (AAPL). Use Pandas to calculate the 50-day moving average, a basic trading indicator.

13. Cybersecurity Analyst

Cybersecurity professionals use Python to automate tasks, analyze malware, and script penetration tests. It’s a powerful tool for sifting through log files to detect a breach or for writing a custom script to test a network’s vulnerability.

• What they do: Defend computer systems and networks from threats, analyze security incidents, and use scripting to automate security operations.
• Average Salary: $100,000 – $140,000 per year.
• Your First Step: Use Python’s scapy library to write a simple network scanner. Write a script that can identify all the devices currently connected to your local network.
  
  

Jobs In Python Summarized!

Profession	Focus Area	Average Salary (USD)	First Step to Start
Data Scientist	Extract insights, build predictive ML models	$125k – $175k+	Learn Pandas and clean a messy Kaggle dataset
Machine Learning Engineer	Build & deploy ML models at scale	$130k – $190k+	Rebuild Linear Regression from scratch with NumPy
Data Analyst	Gather, clean, and visualize data	$75k – $110k	Use Pandas + Matplotlib on Google Analytics dataset
AI Engineer / Specialist	NLP, LLMs, Computer Vision	$140k – $200k+	Run sentiment analysis with Hugging Face Transformers
Backend Web Developer	Server-side code, APIs, databases	$110k – $150k	Build a Flask To-Do List API with 2 endpoints
Full-Stack Developer	Frontend + backend integration	$120k – $165k	Add a simple HTML form to your Flask API
Software Engineer	General-purpose systems/software	$115k – $170k+	Learn OOP in Python (Car class project)
DevOps Engineer	Automation, CI/CD, cloud infra	$120k – $160k	Use boto3 to list AWS S3 buckets
QA Automation Engineer (SDET)	Automated testing with Python	$95k – $130k	Write Selenium script to search on Google
Site Reliability Engineer (SRE)	Reliability, monitoring, scaling	$135k – $185k+	Python script: ping site & log response time
Python Game Developer	Build 2D games, prototypes	$80k – $120k	Build Snake/Pong using Pygame
Quantitative Analyst (“Quant”)	Financial modeling & trading	$150k – $250k+	Use yfinance + Pandas to analyze stocks
Cybersecurity Analyst	Threat detection, scripting, pentesting	$100k – $140k	Use scapy to write a network scanner

How to Choose the Right Python Job For You

Feeling overwhelmed? That’s normal.

Your first job doesn’t define your entire career. The benefit of  jobs involving Python is that the skills are transferable. A backend developer can pivot to DevOps. A data analyst can level up to a data scientist.

The most important step is the first one!

Frequently Asked Questions (FAQs)

Do I need a computer science degree to get a Python job?

No. Absolutely not. While a degree can help, a strong portfolio of projects that demonstrates your skills is far more important to modern tech employers. Bootcamps like Metana specialize in building this job-ready portfolio.

How long does it take to learn enough Python to get a job?

This depends on your dedication. With a focused, full-time effort (like in a bootcamp), you can be job-ready in 3-6 months. Learning part-time on your own can take 6-12 months or more. The key is consistent, project-based learning.

Which Python job is best for beginners?

Data Analyst and QA Automation Engineer are often excellent starting points. They provide a great entry into the tech world, expose you to professional workflows, and build foundational Python skills that you can use to pivot into more senior roles later.

Is Python still in demand in 2025?

Yes, more than ever. Its dominance in AI and data science, two of the fastest-growing fields in tech, ensures that Python skills will remain in high demand for the foreseeable future.

What are the most important Python libraries to learn for jobs?

It depends on the path, but a great starting set includes:

• For Data: Pandas, NumPy, Scikit-learn, Matplotlib
• For Web: Django, Flask, Requests
• For Automation: Selenium, Pytest, Boto3 (for AWS)