How to Get an Entry-Level Data Science Job in 2026: 7 Steps That Work

Last updated: September 2026. Written by Josh Hutcheson, OnlineCourseing editor. Job-posting data, pay figures and programs re-checked at the source on 21 September 2026. See our review methodology.

THE SHORT ANSWER

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Why entry-level data science jobs are hard to get

If every “junior” posting you open seems to ask for experience, you are not imagining it. 365 Data Science analyses more than 1,000 data scientist job postings each year, and its 2026 report found the pattern has reversed since 2025: postings asking for 0–2 years of experience are now the least common experience band, while mid-level roles (2–6 years) remain in highest demand (365 Data Science).

The same analysis shows how the bar has risen on education:

Two things follow. First, the job title “data scientist” is a harder first job than it was a few years ago, so many people now reach it in two steps. Second, anything that counts as experience, including projects, internships and analytics work in a current job, carries a lot of weight. The rest of this guide is about building that experience deliberately.

The realistic routes in

Step 1: Build the skills employers list

Start from what postings actually ask for. The core is consistent:

• Python (pandas, NumPy, scikit-learn) and SQL, which appears in most data job interviews regardless of title. Our guide to data science languages covers the options.
• Statistics and probability: distributions, hypothesis testing, confidence intervals and A/B testing.
• Machine learning: regression, classification, clustering, and above all how to evaluate a model honestly. ML skills appeared in 77% of postings in 365 Data Science’s 2026 analysis.
• Data cleaning and visualization, because real data is messy and results have to be shown clearly.
• Cloud and tooling basics: AWS appeared in 27% of postings and Azure in 16%; Git and a notebook environment are assumed.
• Communication: explaining what a model does, how confident you are and what the business should do.

If you are starting from scratch, our ranking of the best data science courses and our guide to becoming a data scientist without a degree lay out a learning path.

Step 2: Build a portfolio that counts as experience

A portfolio is the main way to answer “do you have experience?” before anyone has hired you. What separates a strong one:

• Real questions, real data. Pick problems someone would pay to answer, using public data from government portals, company APIs or scraped sources, not only the most common tutorial datasets.
• The full process. Show how you collected and cleaned the data, what you tried, what failed and why you chose your final approach.
• A clear result. End with a recommendation or finding in plain language, including the limitations.
• Something deployed. A simple web app, dashboard or API that runs your model shows you can take work beyond a notebook.
• Three to five strong projects beat fifteen small ones. Tailor one or two to the industry you want to work in.

Step 3: Make your work visible on GitHub and LinkedIn

Put each project in a tidy GitHub repository with a README that explains the question, the data, the method and the result in a few paragraphs, so a recruiter can understand it in two minutes without running any code. On LinkedIn, use a headline that names the role you want, list your projects with links, and write short posts about what you learned building them. Recruiters search LinkedIn for skills, so make sure Python, SQL and machine learning appear in your profile in the same words job postings use.

Step 4: Use competitions and open-source work

Kaggle competitions and datasets are a free way to practice modeling against a clear benchmark and to learn from other people’s published notebooks. A good finish is a nice line on a CV, but the bigger value is the write-up you produce afterwards: treat each competition as a portfolio project with its own README explaining what you tried and what you learned. Contributing to open-source data libraries, even documentation fixes, shows you can work in a shared codebase, which many self-taught candidates cannot demonstrate.

Step 5: Get real-world experience before the job

• Use data in your current role. Automating a report or analyzing a business problem at work is genuine experience, and often the easiest route to an internal move.
• Internships and apprenticeships, including paid internships for career changers at some large employers.
• Freelance or volunteer projects for small businesses and non-profits that have data but no analyst.
• Research assistant roles at universities, which suit people with a quantitative background.

Step 6: Apply across the right job titles

Searching only for “junior data scientist” hides most of the openings you are qualified for. Search these titles too:

Tailor your CV to each posting’s skills and wording; our guide on how to write a resume covers how to list projects and certificates. Referrals from people you have met through meetups, online communities or former colleagues get noticeably more responses than cold applications, so treat networking as part of the application process.

Step 7: Prepare for the interview

Data science interviews usually include a SQL or Python screen, statistics and machine learning questions, a product or case round and often a take-home assignment. Be ready to walk through every project on your CV in detail. Our data science interview prep guide covers each round and includes a five-week plan.

A 12-week job-search plan

If you already have the core skills, this plan turns them into applications and interviews. Adjust the pace to the hours you have:

Track every application with the date, title, source and outcome. After a few weeks the pattern tells you what to fix: few responses usually means the CV or the targeting, while interviews that do not convert point to interview preparation.

Is it worth the effort?

For people who enjoy the work, yes. The US Bureau of Labor Statistics reports a 2025 median wage of $120,230 for data scientists and projects employment growth of 35% from 2025 to 2035, much faster than average, from a base of about 275,600 jobs (BLS). The shortage is at the entry level, not in the field overall. Our analyses of whether data science is a good career and whether data analyst is a good career compare the two paths.

Programs that help you build job-ready skills

A structured program will not get you hired on its own, but it gives you a curriculum, projects for your portfolio and a credential for your CV. These are the strongest options:

• IBM Data Science Professional Certificate (Coursera). Twelve courses from Python and SQL through machine learning and a capstone, with nearly 960,000 learners enrolled. The most complete beginner-friendly credential. Our IBM Data Science certificate review covers it in detail.
• Google Advanced Data Analytics Professional Certificate (Coursera). Seven courses covering statistics, Python, regression and machine learning, designed as the bridge from data analyst to data science roles. More than 400,000 learners are enrolled.
• 365 Data Science Data Scientist career track. A subscription-based track from the same company whose job-posting research is cited above, covering statistics, Python, SQL and machine learning with exams along the way.
• DataCamp Data Scientist Certification. An exam-based credential with timed tests, a practical exam on a real dataset and a presentation, useful for proving skills you already have.

See the IBM Data Science certificate →

Frequently asked questions

Can you get a data science job with no experience?

It is possible but hard, because true entry-level data scientist postings are scarce: 365 Data Science’s 2026 analysis of more than 1,000 job postings found roles asking for 0-2 years of experience were the least common. Most people get in by building projects that act as experience, taking an adjacent role such as data analyst first, or moving internally at their current employer.

Do you need a master’s degree to become a data scientist?

Many postings ask for one, but not all. In 365 Data Science’s 2026 job-posting analysis, 31.4% mentioned a master’s degree and 34.7% a PhD, while 16.2% asked for a bachelor’s and 17.7% did not mention a degree at all. Strong skills and a portfolio can offset a missing graduate degree, especially for analyst-to-data-scientist moves.

What is the best first job for an aspiring data scientist?

Data analyst is the most common stepping stone, followed by business intelligence analyst, product analyst and analytics engineer. These roles build SQL, statistics and business experience, and many companies promote analysts into data science positions.

What skills do entry-level data scientists need?

Python and SQL, statistics and probability, core machine learning (regression, classification, clustering, model evaluation), data cleaning and visualization, and the ability to explain results to non-technical colleagues. Machine learning skills appeared in 77% of data scientist postings in 365 Data Science’s 2026 analysis, and cloud platforms such as AWS are increasingly mentioned.

How many projects should a data science portfolio have?

Three to five strong projects are better than many small ones. Each should start from a real question, use real and messy data, show the full process from cleaning to modeling or analysis, and end with a clear written explanation of the result and its limitations.

How much do data scientists earn?

The US Bureau of Labor Statistics reports a 2025 median wage of $120,230 for data scientists and projects employment to grow 35% from 2025 to 2035. Entry-level pay is lower than the median and varies widely by city and industry.

Are data science certificates worth it for getting a job?

They help most as structure and proof of effort, particularly for career changers, and when their projects become part of your portfolio. On their own they rarely get you hired, because many applicants have the same certificates; employers look for evidence that you can apply the skills to a real problem.

The verdict

Landing a first data science job in 2026 takes a plan rather than a stack of applications. Build the core skills, create a small portfolio of real projects that counts as experience, make it visible, and apply across analyst, product, BI and analytics-engineering roles as well as junior data scientist posts. For most people the first job is a stepping stone; one to three years in an adjacent role is a normal and effective route into data science.

See the Google Advanced Data Analytics certificate →

Related guides: Become a data scientist without a degree · Data science interview prep · Best data science courses · Is data science a good career? · Is data analyst a good career? · How to write a resume