So you decided to pursue a data science career path and are curious about what your career may look like in a few years?
Or, maybe, you're still deciding if a data science career path is the right track for you.
Either way, we've got you covered! In this article, we'll be sharing:
- 1Data Science Careers To Pursue
- 2The Data Science Career Path: Roles, Skills, & Progression
- 3Data Science Salaries (Entry-Level, Mid-Level, & Senior Level)
- 4Level-Up Your Game: How To Step Into A New Data Science Role
Data Science Careers To Pursue
Data science offers a plethora of career opportunities across various industries, each requiring a unique set of skills and expertise.
For example:
- A data science role at a tech company might focus on machine learning, predictive modeling, and artificial intelligence to drive product innovation and improve user experiences.
- A data science role at a retail corporation could emphasize customer analytics, sales forecasting, and inventory optimization to enhance business operations and profitability.
- A data science role at a financial institution could provide opportunities in fraud detection, risk management, and algorithmic trading to secure assets and increase earnings.
Not all companies will demand every specialty, but every specialty has a place within many different companies!
So, if you're thinking about which data science career you should pursue, it might be a good idea to first consider what kind of company you would like to work for.
Maybe you'd like to work for a leading-edge tech company.
Maybe you're aiming for a global leader in retail.
Or, maybe, you'd like to enter the finance sector.
You will find different data science opportunities with varying scopes and challenges. Having clarity about where you'd like to be within a certain time frame is key to deciding which path is right for you.
Most skills you need for a data science career are transferable across different roles, industries, and companies. The purpose of the exercise is so you can look at the opportunities on your horizon and decide where you'd like to be in the short term and eventually down the road!
💡 Pro Tip:
If you're struggling to find out where you'd like to be, check out the article “What Should I Do With My Life? A Step-By-Step Guide” and read it from top to bottom. If “data science” still feels like the right path, head back here and continue reading!
Once you've set your mind on your career path, it's time to understand what roles might be a good fit for you.
The Data Science Career Path: Roles, Skills, & Progression
We've selected the most common data science roles for each level and their job descriptions. Take a look below!
Entry-Level Data Science Roles
Junior Data Scientist
📝 Job Description: Focuses on data cleaning, initial analyses, and developing basic machine learning models to support more senior data scientists.
✅ Skills Required: Proficiency in Python or R, basic understanding of SQL and data visualization tools.
⬆️ Possible progression: Can progress to a Data Scientist role.
Junior Machine Learning Engineer
📝 Job Description: Assists in the development and implementation of machine learning algorithms under the guidance of senior engineers.
✅ Skills Required: Knowledge of Python, familiarity with TensorFlow or PyTorch, and a basic understanding of algorithms.
⬆️ Possible progression: Can advance to a Machine Learning Engineer position.
AI Specialist
📝 Job Description: Applies artificial intelligence techniques to solve simple business problems and supports the development of AI applications.
✅ Skills Required: Good programming skills, understanding of basic AI principles and frameworks like TensorFlow or PyTorch.
⬆️ Possible progression: Can move into a Senior AI Specialist role.
Mid-Level Data Science Roles
Data Scientist
📝 Job Description: Develops complex predictive models and algorithms to derive insights from large datasets that influence strategic decisions.
✅ Skills Required: Advanced analytical skills, proficiency in machine learning libraries, and strong knowledge of statistical tools.
⬆️ Possible progression: Can progress to Senior Data Scientist.
Machine Learning Engineer
📝 Job Description: Designs and deploys scalable machine learning solutions that integrate with company products and systems.
✅ Skills Required: Expertise in ML frameworks, strong programming skills, and experience with cloud technologies.
⬆️ Possible progression: Can advance to Senior Machine Learning Engineer.
Senior AI Specialist
📝 Job Description: Manages complex AI projects and develops advanced AI models that are critical to business functions.
✅ Skills Required: Deep knowledge in AI, experience with neural networks, and proficiency in several AI development tools.
⬆️ Possible progression: Can move into an AI Team Lead role.
Senior-Level Data Science Roles
Senior Data Scientist
📝 Job Description: Leads data science projects from conception to execution, providing analytical leadership and insight into complex data challenges.
✅ Skills Required: Extensive experience in data science, expertise in predictive analytics and machine learning, and strong leadership abilities.
⬆️ Possible progression: Can advance to Lead Data Scientist.
Senior Machine Learning Engineer
📝 Job Description: Oversees the development of sophisticated machine learning models, ensuring they perform effectively at scale.
✅ Skills Required: Advanced technical skills in building and deploying large-scale ML systems, strong project management, and problem-solving skills.
⬆️ Possible progression: Can progress to Lead Machine Learning Engineer.
AI Team Lead
📝 Job Description: Directs a team of AI specialists, setting strategic priorities and integrating AI technologies into business processes.
✅ Skills Required: Leadership, deep understanding of AI applications, and ability to manage multiple AI projects.
⬆️ Possible progression: Can move into an AI Manager role.
Leadership Data Science Roles
Lead Data Scientist
📝 Job Description: Sets the strategic direction for data science initiatives and oversees the data science team's operations and projects.
✅ Skills Required: Expert-level knowledge in data science, strategic planning abilities, and exceptional leadership skills.
⬆️ Possible progression: Can advance to a Director of Data Science or Chief Data Officer role.
Lead Machine Learning Engineer
📝 Job Description: Manages the machine learning engineering team, developing high-level strategies for ML implementation and innovation.
✅ Skills Required: Profound technical expertise in machine learning, leadership, and ability to drive ML projects from ideation to deployment.
⬆️ Possible progression: Can advance to Head of Machine Learning or similar roles.
AI Manager
📝 Job Description: Oversees all AI operations within the company, ensuring they align with strategic business goals and manage a team of AI professionals.
✅ Skills Required: Strong management skills, strategic insight into AI applications, and a proven track record in leading AI initiatives.
⬆️ Possible progression: Can move up to Chief Technology Officer or Chief Data Officer, depending on the company's structure.
Data Science Salaries (Entry-Level, Mid-Level, & Senior Level)
Now that we’ve covered the most common data science career paths, you might be asking yourself what the pay range is for each role.
To answer this question, let’s head over to one of our favorite tools for salary research: Glassdoor.
Glassdoor is one of the world’s top job and recruiting websites, where users can anonymously provide information about their companies – including their current salary. Glassdoor provides an average salary range for various roles based on the information sent by its users.
Data Science Roles:
- Junior Data Scientist: $80K – $117K / year base pay
- Data Scientist: $96K – $147K / year base pay
- Senior Data Scientist: $136K – $188K / year base pay
- Lead Data Science: $143K – $195K / year base pay
Machine Learning Roles:
- Junior Machine Learning Engineer: $93K – $147K / year base pay
- Machine Learning Engineer: $101K – $152K / year base pay
- Senior Machine Learning Engineer: $135K – $183K / year base pay
- Lead Machine Learning Engineer: $135K – $183K / year base pay
AI Roles:
- AI Specialist: $85K – $134K / year base pay
- Senior AI Specialist: $94K – $159K / year base pay
- AI Team Lead: $131K – $206K / year base pay
Level-Up Your Game: How To Step Into A New Data Science Role
Whether you're looking for an entry-level job in data science or aiming to advance in your data science career, it's important to know that you don't necessarily need a graduate degree or specialization to move up the data science career ladder. Your unique experience and skills can go a long way, as long as you learn how to sell them!
Here are a few tips and tricks that will help you land your next data science role.
1. Run A Resume Scan To Find Out Skill Gaps In Your Target Role
Wondering if you are the right fit for that target role you've been eyeing?
We've got you covered.
Here's a simple, step-by-step guide to find out if you have the skills to land a job in data science:
- 1Copy the job description of the data science role that sparked your interest
- 2Head over to ResyMatch.io (or use our shortcut below)
- 3Grab a copy of your most updated resume
- 4Upload your resume on the left side
- 5Paste the job description on the right side
- 6Hit “Start Resume Scan”
Boom! ResyMatch will compare and score your resume against the job's description and identify missing skill gaps, such as:
ResyMatch will also make sure your resume is ATS compatible (ATS is a software that recruiters often use to track candidates through the hiring process). And of course, ResyMatch will provide suggestions and outline best practices you can use to edit and improve your resume!
Use our shortcut below to get started:
Scanning...
2. Update Your Resume With Compelling Resume Bullets
After you've compared your resume against your target job description, you will likely find yourself in one of two scenarios:
1. You have some missing skills that you'll need to master before taking on a new role — if that's the case, you can take action and start building those skills through online courses and a portfolio strategy.
OR…
2. You have already mastered most of the skills — if that's the case, all you need to do is update your resume with compelling resume bullets, leveraging the keywords found in your resume and job description scan.
To get started, you'll want your resume bullets to have just the right amount of hard and soft skills, action words, measurable results, and common words.
This means a compelling resume bullet for someone applying for a data science role might look something like this:
Enhanced predictive model accuracy by 30% through advanced algorithm optimization, resulting in a 20% boost in customer retention.
This bullet focuses on hard and soft skills specific to data science, while also showcasing measurable results!
To help you write the perfect resume bullet, we've created ResyBullet.io, a free resume bullet analyzer that helps you write your resume in a way that grabs attention and illustrates value. Simply copy and paste your resume bullet below to begin your analysis:
ResyBullet will analyze and score your resume bullet and give you actionable insights for improvement.
Here's how our resume bullet scored on ResyBullet:
If you're a visual learner, check out our video that walks you through the step-by-step of writing a crazy-effective resume bullet:
3. Build A Visually Appealing, ATS-Friendly Resume
Now that you've updated the content on your resume, it's time to transfer it over to an awesome layout that's also ATS-friendly.
We recommend using a resume template so you can save the time you'd normally spend designing your resume and instead allocate it to your job search.
You can use ResyBuild.io, a free AI resume builder, to easily build and customize your resume in no time. Just pick one of the templates below and get started:
Free Job-Winning Resume Templates, Build Yours In No Time.
Choose a resume template below to get started:
Choose from 8 proven templates and easily create, edit, and customize your resume. ResyBuild's AI assistant also helps you craft personalized, job-winning bullets in a single click. Simply add your experience, hit “Optimize,” and watch the magic happen.
4. Don't Apply Online — Do This Instead
The most common next step in the process is to start applying for data science roles.
While applying online can absolutely be the next step in your job search, the truth is, it will only get you so far. In fact, only 2% of resumes submitted for the average open role end up reaching the interview stage.
Sure, optimizing your resume will boost your chances of being a part of that 2%.
But some estimates, like this one from the Wall Street Journal, show that 80% of hires come from referrals.
This means your best shot at landing the role you've been eying is through networking.
And no, we're not talking about attending conferences, events, and meetups. We're talking about a fresh approach that really builds relationships and gets your resume at the top of the resume pile at companies like Microsoft, Google, Uber, and more.
You can read all about it in our flagship guide for effective job searching:
Read More: How To Get A Job Anywhere Without Applying Online
Final Notes
Whether you're just starting out or looking to advance in your data analyst career, getting clarity on the possibilities ahead of you can help you navigate your career with more ease.
Just don't forget: your career path isn't set in stone. Jobs and career goals will often evolve with life transitions. Always remember that your career should adapt to fit your life and not the other way around.
