Today, I’ll share with you the best ways to find your first job in Data Science. I’ve learned these tips over nine years of experience in the field.
I’m assuming you’ve already acquired the fundamental skills (you can read about them here) and decided it’s the right time to enter the job market. So, let’s get started!
Find Your Motivation and Set a Goal
Finding your first job in Data Science will often require effort and perseverance, especially if this is a new field for you.
Don’t expect to land your first role within a few weeks. A more realistic timeline is a few months, or it might even take over a year.
To stay motivated and not give up quickly, you need a strong *why* — why do I want to work in Data Science?
The more reasons you list, the greater your motivation will be.
It’s best if your motivation isn’t purely financial. Write down what else drives you.
Maybe you enjoyed math in school? Perhaps you find satisfaction in organizing and calculating data in Excel?
Do you already work in a related field, like programming, engineering, or accounting?
Or maybe you just want to be at the forefront of innovation, creating artificial intelligence?
Remember, you have the option to choose between different related roles, such as Data Analyst, Data Scientist, Machine Learning Engineer, or Data Engineer. That’s why it’s important to understand your skills and interests well, so you can aim for a role that will be truly satisfying for you.
At this stage, browse open positions at companies (on platforms like JustJoinIT, NoFluffJobs, or LinkedIn) and pick roles that seem ideal for you. Not necessarily for right now — select your “dream” position you want to aim for eventually.
Prepare a Strong CV and Customize It with ChatGPT
There are plenty of online guides on how to create a good CV for Data Science jobs.
Here’s a crucial tip: prepare one comprehensive CV containing all your skills, certifications, experience, volunteer work, interests, etc. Then, always tailor your CV to match the specific job offer.
The worst thing you can do is send out the same generic CV to multiple job postings without even reading the job requirements.
Here’s where LLMs come in handy. You can upload your full CV to ChatGPT and paste the job description from the portal. Then, prompt ChatGPT with something like:
”Customize my CV to match the job requirements. Highlight and expand on the skills that are relevant. Omit unnecessary details. Please don’t invent new skills — stick only to what’s in my CV.”
Of course, you should always read through the AI-generated CV and double-check for inaccuracies before sending it out.
Optimize Your LinkedIn Profile
LinkedIn is now the go-to platform for professional networking. If you don’t have an account yet, it’s time to create one. Check out some tutorials on how to build your profile effectively for the Data Science industry.
In short, you need to fill out all sections, including:
– Profile picture and headline
– Experience with project descriptions and skills
– Skill endorsements (validated through tests or by other users)
– Recommendations from colleagues or supervisors
– Education, certifications, and training
Avoid using AI-generated graphics or content unless you’ve heavily customized them.
Set your profile to show that you’re open to new job opportunities.
Where to Look for Your First Data Science Job?
Start by exploring platforms with job postings. Popular ones include NoFluffJobs, JustJoinIT, Glassdoor, and LinkedIn Jobs.
Most companies actively seeking employees will post their openings there.
There are also smaller platforms like Recbot in Poland, which connect employers with candidates and offer additional useful tools.
However, it’s also worth being proactive. Research companies where you’d like to work, check if they have a “Careers” page with open positions, or reach out to them directly with a proposal.
Sometimes companies aren’t actively hiring but are open to interesting applications. They may keep your contact information and reach out when a suitable position opens up.
When reaching out, personalize your application. Don’t approach it with “I’m looking for a Data Science job, will you hire me?” Instead, say something like:
”I see that you’ve been successfully completing projects in the energy sector. While I’m just starting out in Data Science, I’ve been working in this industry for 10 years, which could be a great asset to your team.”
Explain why they should hire you and show that you’re genuinely interested in their company.
Free Internships in Data Science
If possible, consider applying for a free internship. It doesn’t have to be full-time; even 2–3 hours a day can work. Show the employer your projects and demonstrate that you can work independently.
This is not only a chance to list your first Data Science job on your CV and LinkedIn but also an invaluable opportunity to test yourself in a real business environment.
Continuous Learning
I recommend not separating your development into distinct stages like first learning, then job hunting. It’s better to combine these activities.
Once you’ve mastered the basics (algorithms, libraries, programming language), it may be challenging to identify what else you need to learn to land your first job.
Participating in recruitment processes will help you see where you stand and identify areas for improvement.
Build Projects and Create a Portfolio
While searching for a job, work on your own projects to build a portfolio.
Where can you find ideas?
Start with simple projects on Kaggle (or participate in ongoing competitions if you feel ready).
It’s even better to explore open datasets, and create unique projects.
From the outset, think about the business context — who could benefit from this project? What value will it bring?
Host the code for these projects in a public repository to showcase in your CV and during interviews.
Network and Find Mentors
A supportive community of like-minded individuals is incredibly helpful for growth.
There are numerous opportunities to connect with people who already work in Data Science or, like you, are building their portfolios.
You’ll also find excellent networking opportunities at industry events such as PyData, Data Science Summit, MLinPL, or Warsaw IT Days.
Prepare for the Recruitment Process
The recruitment process for Data Science roles varies between companies but typically includes the following stages:
1. Submitting your CV.
2. Initial interview with HR (to check your English skills, experience, availability, and salary expectations).
3. Recruitment task: e.g., analyzing a dataset or creating a simple ML model.
4. Technical interview: questions about Python, SQL, or Machine Learning.
Sometimes, you’ll have a few days to complete the recruitment task; other times, it’s a timed test. Technical interviews can range from general questions to live coding sessions.
During your first conversation with HR, ask about the recruitment process and the skills that will be assessed at each stage.
How to Excel in an Interview?
Make sure to get enough sleep and stay relaxed. While some stress is inevitable, remember that every interview helps you improve.
Most importantly, show genuine engagement and honesty. Remember, the interview is not just for the employer to assess you, but also for you to evaluate the company.
Prepare thoughtful questions for the hiring manager. This will not only help you learn more about the company but also leave a good impression.
What If You Can’t Find a Job in Data Science?
Don’t get discouraged. Give yourself at least a year to search for your first job. It’s the hardest to find the first one.
Use this time to refine your skills and work on open-source projects, as you might not have the same opportunity later.
Remember, the market has its fluctuations, but Data Science is here to stay. Companies are collecting more data than ever, and leveraging it in business processes is still in its early stages.
