If you’re interested in becoming a data scientist, there may be no better time to make your move. The US Bureau of Labor Statistics (BLS) projects employment of data scientists will grow 34% through 2034. That’s much faster than the average for all occupations.
The demand is real. And so is the competition, but landing a job is very doable with a clear plan.
This guide walks you through how to get a job in data science, from building the right skills to landing your first offer.
Key Points
- Data scientists don’t always need a formal degree. Nearly half of data science job postings don’t require a formal degree, according to a 365 Data Science analysis of job postings.
- Python leads every other language in data science job postings at 57%, per 365 Data Science’s analysis. That’s followed by R at 33% and SQL at 30%.
- Real-world experience is key to success. Internships, volunteer projects, and freelance work all count, and they produce portfolio material at the same time.
- Your resume matters, but a well-rounded portfolio of projects with measurable outcomes is non-negotiable for pursuing data science roles.
- Data science interviews are often highly technical and will test your skills. Practicing in advance can help you knock your interview out of the park.
Step 1: Get a Relevant Degree or Certification
Many data scientists have bachelor’s degrees in a quantitative field like computer science, statistics, or mathematics. Some get master’s or doctoral degrees. However, a 365 Data Science analysis of 1,000 job postings found that only 47.4% of data science roles actually require a degree.
If you have a relevant degree, the priority is translating coursework and related experience into portfolio-ready projects. That tends to matter more to recruiters than additional credentials.
If you don’t have a relevant degree, credible certifications can help fill the gap. The Google Data Analytics Certificate or Coursera’s IBM Data Science Professional Certificate are both well-regarded. Or you could take part in an online bootcamp like General Assembly.
Whatever route you take, applying what you’ve learned to open-source or personal projects can also go a long way with recruiters and hiring managers.
Step 2: Learn the Core Technical Skills
For data scientists, the core skills break into 2 tiers: what you need to get hired, and what separates good candidates from the best candidates.
Must-Have Skills
Data scientists spend their days analyzing large, messy datasets to uncover insights that drive smarter business decisions and innovation at scale. That requires analytical tools and techniques.
According to the BLS, the foundational technical skills include mathematics and statistics, machine learning (ML) fundamentals, data-oriented programming languages, data visualization tools like Tableau, Matplotlib, and Power BI, and data collection and cleaning.
Of those requirements, programming gets a lot of attention on the hiring side. Based on 365 Data Science’s job posting analysis, the top coding languages for data scientists are:
- Python (appeared in 57% of job listings): This is the primary coding language. Get familiar with its core libraries—pandas (data manipulation and analysis), NumPy (numerical calculations), scikit-learn (machine learning).
- R (33%): R is frequently used in statistical computing and graphics, as well as data analysis.
- SQL (30%): Mostly used in data querying, this is the language of databases. Start with SQL joins, window functions, and aggregations.
Note: An in-depth understanding of 1 or 2 languages matters more than knowing every tool. You can always pick up others as your career advances.
Good-to-Have Skills
The next tier includes more advanced machine learning and AI concepts, cloud platforms like Amazon Web Services (AWS) or Azure, and Apache Spark for big data work. Note that the same 365 Data Science research found Azure appearing in 28.5% of job postings, so cloud fluency is increasingly expected.
Soft skills can also help you stand out. The ability to communicate findings clearly to both technical and non-technical audiences is a real differentiator. So is knowing how to turn raw analysis into a coherent visual story.
Some employers also look for domain knowledge specific to their industry. A financial services firm, for instance, might value candidates who understand the business context behind the data.
Where to Learn
As we mentioned earlier, many data scientists have college degrees in fields like mathematics, statistics, computer science, business, or engineering, but not all do. There are plenty of free and paid resources to help you build the skills you need to become a data scientist, including:
- Coursera: Data science specialization
- Kaggle Learn: Python, data visualization, machine learning
- EdX: Free and paid online courses and certificates from top colleges)
- Fast.ai: Hands-on exercises for machine learning and deep learning
- FreeCodeCamp: Free coding courses, Python certification, JavaScript, Git, data analysis
Start with one that interests you the most and go from there.
Step 3: Get Comfortable with Data Tools and Environments
Theoretical knowledge is great. But getting a data science job requires showing employers you’ve got actual experience using those tools. Real-world experience or mock projects are ways to build that experience. And both can go straight into your portfolio and serve as useful talking points in interviews.
Key Tools to Get Hands-On With
A few tools come up consistently enough in data science work that it’s worth getting comfortable with them early:
- Jupyter Notebooks in VS Code for development
- GitHub for portfolio hosting
- Git for version control (for open-source, collaborative projects)
- Pandas for working with datasets (Python)
- NumPy for data wrangling and manipulation (Python)
- Matplotlib for data visualization (Python)
- Seaborn for data visualization (Python)
- Google Colab for free GPU access and shareable notebooks (Python)
Again, you don’t have to know everything. However, fluency in at least 1 programming language and 1 visualization language is a great starting point.
Step 4: Build a Portfolio of Real Projects
A strong portfolio is a must for getting a job in data science. But what should you include, and how do you best showcase your skills?
Start with real projects. These could be from your college days or internships. They could also come from personal projects or freelance work. Open-source projects also carry weight if you present them effectively.
Don’t include every project you’ve ever worked on, though. Choose 3 to 5 of your most polished projects demonstrating measurable results. Where possible, tailor them to the domain you’re targeting, whether that’s financial services, healthcare, or something else entirely.
What Makes a Strong Portfolio Project
A strong portfolio project showcases your skills in a way that’s relevant to the job. It should show employers how you approach data and solve problems. It should also clearly convey your process and the tools you used, as well as specific results.
Standout portfolio projects should:
- Use a real, publicly available dataset (e.g., Kaggle, data.gov, UCI ML Repository)
- Solve a clear question with a defined outcome (prediction, classification, analysis)
- Include the full pipeline (data cleaning, exploratory data analysis (EDA), modeling, visualization, and interpretation)
- Be hosted on GitHub with a clean README (ideally paired with a short blog post or write-up)
Your portfolio is the best way to differentiate yourself from the crowd, especially if you don’t have prior data science job experience. So, take the time to make it the best you can.
Step 5: Gain Hands-On Experience Through Internships or Freelance Work
If you’re coming from a related field, figuring out how to get a data science job might not be as hard as you think. Industries like software engineering or data analytics transfer well into data science.
Starting from scratch is harder, but it is possible to bridge the gap between learning and employment. Seek out internships where you can learn and put your theoretical skills into practice. Volunteer for data science projects at nonprofits to gain valuable hands-on experience. Participate in Kaggle competitions to network with others while building your tech skills. You can even take on small freelance projects.
Even informal experience with real data and stakeholders can be more valuable than more coursework. And these experiences can eventually translate into entry-level career opportunities.
Ready to get started? Learn about current and upcoming internships and co-ops at Intuit.
Step 6: Network and Engage With the Data Science Community
According to a 2026 Express Employment Professionals-Harris Poll survey, networking led to a referral for 39% of job seekers, an interview for 36%, and a job offer for 32%. A staggering 84% say networking matters; 92% of hiring managers agree.
The data science community has several natural gathering points where you might focus your networking efforts:
- LinkedIn: Connect with data scientists. Follow industry leaders or companies and comment thoughtfully on posts.
- Local meetups and conferences: Find out if there are any data science events in your area. They’re a great way to link up with other professionals at different stages of their careers. If travel is an option, consider an international meetup like PyData.
- Online communities or events: No matter where you live, you can always connect online. Show up in online communities like Kaggle forums or Slack groups. The subreddit “r/datascience” is another option for sharing your experiences and learning from others. You could also attend digital conferences like Strata.
Sharing your own work publicly—even beginner projects or challenges you’ve overcome so far—can build a reputation. Do it consistently enough, and you might even find yourself with inbound opportunities.
Step 7: Tailor Your Resume and Apply Strategically
Be thoughtful and strategic when applying to data science jobs. Ten highly specific, relevant resumes are more likely to grab attention than 100 generic resumes.
Also, keep in mind that 71% of hiring managers use applicant tracking software (ATS) to screen candidates. To improve your chances of passing that initial screening:
- Emphasize the most important details first, like skills, related experience, and projects
- Include the right keywords from the job description
- Be clearly organized with consistent formatting; text-based PDFs or Word docs are best
Resume Essentials
Your resume needs to show impact with numbers, not just list tools. Here’s how to write 1 that will help you get a job in data science:
- Lead with a skills section: Emphasize the fundamentals like Python, SQL, ML libraries, and data visualization tools.
- Emphasize projects and outcomes: Clearly indicate the problem, process, and solution. Quantify those outcomes whenever possible. Use accuracy percentages, number of records analyzed, time saved, or whatever other measurable outcomes you’ve got. Be specific.
- Link to your GitHub portfolio: Include a link in your resume and supporting documents, like a LinkedIn profile or cover letter. If you’ve got published notebooks or blog posts, share those, too.
- Tailor every summary: Your summary line should be geared toward the specific role. Mirror the language in the job description.
- Include education: Mention any formal or informal education in your resume. This can include professional courses or certification.
- Include relevant experience: Professional work experience is important. But if you don’t have that, volunteer work or internships are still useful.
Step 8: Prepare for Data Science Interviews
Data science interviews tend to be thorough. You might even go through 3 to 5 rounds for a single role, covering technical depth and how you think and communicate under pressure.
What to Expect in a Data Science Interview
These are the common stages of the interview process:
- Coding screen (preliminary interview): This might be automated, but not always. These interviews test your core tech skills in SQL (window functions, joins, aggregations), Python (data manipulation), and so on.
- Statistics and probability questions: Employers want to know how you handle real data problems. Be prepared to answer questions about A/B testing, hypothesis testing, Bayes’ Theorem basics, and distributions.
- Machine technical interview: You’ll generally need to know at least the basics of machine learning and AI. This includes model selection, bias-variance tradeoff, cross-validation, and evaluation metrics.
- Take-home assignment or case study: Roughly 25% of interviews require a take-home project. For example, you might need to analyze and report on a business’s product growth and performance.
- Behavioral interview: These interviews primarily test you on how you’ve handled real-world situations (like resolved a workplace conflict or communicated findings to stakeholders).
The more you practice in advance, the more confident you’ll be when the time comes to prove yourself to a hiring manager.
Start Your Data Science Career at Intuit
The path to a data science job is clear enough: Build the technical fundamentals and develop a portfolio of real projects, and then put yourself in front of the right people through networking and strategic applications. None of it happens overnight, but each step builds on the 1 before it.
Intuit’s data science teams work on problems affecting over 100 million customers across TurboTax, QuickBooks, Credit Karma, and Mailchimp. If you’re ready to explore what that looks like in practice, browse open data science jobs.
FAQs
Do all data scientists need to know machine learning?
Machine learning is quickly becoming a crucial skill for data scientists. A 365 Data Science study found that 38% of data science roles now call for it.
What other careers can data scientists transition into?
You might start out as a junior data scientist or business analyst. As you advance, you can always move into more specialized roles like data engineer or product manager.
Do all data scientists need advanced degrees?
Not every data scientist needs a college degree, but most have a bachelor’s degree in a related field, such as computer science or mathematics. Just under half of all data science roles (47.4%) require a degree, according to 365 Data Science.