How to Get A Job in Data Science/Machine Learning With No Previous Experience

Take charge of your job search

Photo by Eric Prouzet on Unsplash

So you want to get a job in data science or machine learning, but all the jobs — even the entry-level ones — require experience. How are you supposed to get the experience you need if no one will hire you without experience?

What we need to do here is create our own opportunities.

The Importance of a Portfolio

Since you can’t demonstrate your skills through previous job titles, you’ll need a strong portfolio. But, not all projects are created equal. Let’s explore your options, from least to most effective:

Follow-Along Projects: The Starting Point

While Udemy and Coursera tutorial projects are great for learning the basics, they won’t help much with job hunting. These guided projects don’t require you to source datasets, overcome real challenges, or do independent problem-solving. They simply don’t demonstrate the skills employers need, even for entry-level positions (unfortunately).

Course Work: A Step Up

Schoolwork and certificate program capstones are a bit better than tutorials. They usually involve some troubleshooting and possibly finding your own datasets. However, they’re often generic and still offer significant guidance. While these are better for learning, they rarely impress employers since they’re typically small, one-off projects that don’t reflect real-world work.

Slightly More Effective: Being Good at Kaggle

Kaggle competitions offer more value than coursework, though they still use well-structured, clean datasets. However, if you can achieve high rankings, it becomes a nice differentiator on your resume. While not ideal, it’s a stronger option than the previous two.

Open Source and StackOverflow: Building Real Credibility

Contributing to open source projects or being active on StackOverflow takes things up a notch. Open source work is particularly valuable because you’ll interact with experienced developers and prove you can create useful solutions. All you have to do is browse GitHub Issues for libraries you use regularly, and try fixing problems. The feedback you’ll get from more senior developers on pull requests is excellent learning, and being able to say “contributed five pull requests to [well-known library]” looks great on a resume.

Similarly, consistently helping others troubleshoot on Stack Overflow demonstrates practical problem-solving skills that can catch employers’ attention.

Even Better: Self-motivated Projects

Now we’re getting to the really effective projects — ones you conceive and execute independently. Look for problems or interesting questions in your daily life, then source your own dataset (ideally via an API) and create an end-to-end solution. For machine learning roles especially, try to incorporate MLOps skills to show you understand the full development lifecycle.

Best for Portfolios: Real Work for Real Clients

The most valuable portfolio pieces come from solving real problems for real clients — even if you do it for free. Here’s how to find these opportunities:

1. Start at Your Current Job — Even if you don’t work in tech, look for opportunities to apply data science/ML in your current role. Someone working at a grocery store, for instance, might build a computer vision model to predict when shelves need restocking. This kind of project might even help you transition gradually toward your target role within your current company (which is typically an easier path for that first job).
2. Network with Friends and Family- Know anyone who owns a business? Approach them with specific suggestions for how data science/ML could help. Propose concrete solutions like customer lifetime value models or market segmentation analysis and explain how this can help their business to grow.
3. Reach Out to Small Businesses - Research local businesses, identify their likely challenges, and pitch specific data science/ML solutions. Many would welcome free help if you can clearly explain the benefits.
4. Partner with Nonprofits — Nonprofits often work with pro bono consultants and make great portfolio-building partners. Organizations like DataKind specifically connect data scientists with nonprofits needing technical help. These projects can teach you valuable new skills while helping you make connections and build your portfolio.
5. Consider Freelancing While more competitive, freelancing can be another path to experience. Start small on platforms like Upwork or Fiverr, then work your way up to bigger clients and better rates.

Marketing Your Experience

Now that you’ve built some real experience, it’s time to sell it effectively. Here’s how to showcase your skills:

Resume/LinkedIn Strategy

Update your resume and LinkedIn strategically. While conventional advice suggests putting experience at the top, if your current role isn’t relevant to data science/ML, place your skills and projects first instead.

For those client projects you completed — even the free ones — list them as real jobs (because that’s exactly what they were). If you did data science work for a small business, you were effectively a Data Scientist doing freelance work. Don’t hesitate to list that title.

In your LinkedIn summary section or bio, present yourself confidently as a Data Scientist or Machine Learning Engineer. Drop words like “aspiring” — instead, say something like “Data Scientist with experience in [specific areas] and [key skills from your projects].”

Online Presence

As you complete portfolio projects, share them actively on LinkedIn and/or Medium. Keep your network updated regularly to show that you’re already an active Data Scientist or Machine Learning Engineer — not someone waiting for their career to begin.

Make sure your GitHub is well-organized and includes all your portfolio work. This isn’t just about having projects there — it’s about presenting them professionally. So:

• Keep everything clean and organized
• Avoid messy elements like blocks of commented-out code in production-quality work
• Include good docstrings/comments/type hinting etc.
• Structure everything to reflect how you’d work in a professional environment
• Have a clear and engaging README on each project, and your profile page.

Landing Interviews

Now that you have some experience and your marketing materials are ready, it’s time to get your application in front of people who can actually hire you.

Networking

Yes, you already know you’re supposed to network — so I won’t belabor this point too much. Ideally, focus on in-person meetups, though active online communities can work well, too. The key is building genuine relationships that could lead to referrals down the line.

Bypassing Application Screening

One major challenge when you have limited experience is that resume screening tools often filter you out immediately. A effective way around this — and just good job search strategy in general — is reaching out to recruiters or hiring managers directly.

Keep in mind that recruiters receive many messages, so make yours count by keeping it short, respectful, and focused:

• Start with a brief introduction of yourself, your skills, and experience.
• Explain specifically why you’d be a great fit for their company. Identify a problem they are likely to have, and specifically how you would address this.
• If you have experience relevant to their company’s challenges, highlight it explicitly.
• Include a link to your portfolio.

As with everything in your job search, the more research and customization you can do, the better. When you’ve thoroughly researched the company and identified their likely challenges, you can explain exactly how your skills and experience could solve their problems. This makes recruiters far more likely to pay attention to your message.

Looking Beyond Tech Companies

Don’t limit yourself to tech companies — many different industries need data science and machine learning expertise now. If you have experience in another field, use it to your advantage. For example, your background could make you perfect for a data science role in healthcare or another specialized sector.

Interview Preparation

Once you start landing interviews, be prepared to work extra hard. Interview prep is already challenging for these roles, and with limited experience, you'll need to prepare about ten times harder than other candidates.

For more interview prep tips, check out this post!

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Remember, transitioning into data science or machine learning is absolutely achievable. It takes persistence, optimism, and courage, but by building genuine experience and demonstrating real value to potential employers, you can break into the field successfully.

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