A 2024 Burning Glass analysis found that 35% of job postings labeled "entry level" in data science required at least two years of prior experience. If you've been applying and getting nothing back, that's probably why — not your qualifications. Data science entry level jobs exist, but the path to them is more specific than most guides admit.
This guide skips the generic "learn Python and SQL" advice and focuses on what actually differentiates candidates who get hired in 90 days versus those who apply for 18 months and give up.
What "Entry Level" Actually Means in Data Science
Companies use "entry level" to mean two very different things. The first is genuinely no-experience-required: they'll hire a recent grad or bootcamp grad who can demonstrate foundational skills in a take-home assessment. The second is "junior" — they want 1-2 years but call it entry to keep salaries down.
Knowing which you're looking at saves weeks of wasted effort. Signs of a genuinely entry-level role:
- Job description mentions a structured onboarding or rotation program
- The company is large enough to have a data team of 10+ (they can absorb learning curves)
- The posting lists "or equivalent experience" next to the degree requirement
- A technical screening replaces the resume screen (they're testing skills, not credentials)
Signs it's "junior" masquerading as entry:
- Lists 3+ years of Python/ML experience under "nice to have"
- Asks for production model deployment experience
- The company has a 3-person data "team" — they need someone who ships on day one
Data Science Entry Level Jobs: The Actual Titles to Search
Most people type "data scientist" into LinkedIn and miss 60% of relevant openings. Entry-level data science work gets posted under several titles, and some of them convert to full data scientist roles faster than others.
Data Analyst
The highest-volume entry point. SQL-heavy, dashboard-heavy, stakeholder-facing. Median salary at entry: $62,000–$78,000. Companies promote analysts to data scientists internally faster than they hire external candidates — which makes this the most reliable ladder into the field.
Business Intelligence Analyst
Overlaps heavily with data analyst but skews toward reporting infrastructure (Tableau, Power BI, Looker). Less Python, more SQL and visualization. Salary range is similar. Worth targeting if your background is in business or finance.
Junior Data Scientist
Fewer postings than analyst roles, but more model-building day-to-day. Usually requires a portfolio showing at least one end-to-end ML project. Median entry salary: $80,000–$95,000 in most US markets.
Data Science Associate / Analyst I
Large financial services firms (JPMorgan, Capital One, American Express) and tech companies use these titles for structured graduate hiring programs. Application cycles open August–October for May/June starts. If you're a recent grad, these are worth prioritizing.
Marketing Data Analyst / Product Analyst
Domain-specific data roles at mid-sized companies. Lower bar for ML skills, higher bar for business context and A/B testing knowledge. These roles transition to data science in 18–24 months more reliably than generalist analyst roles at the same company level.
Skills That Actually Get You Through the Door
The skills gap between "learning data science" and "employable in data science" is real, but it's narrower than most bootcamps suggest. Here's what hiring managers actually screen for at the entry level:
SQL — Non-Negotiable
Every data science entry level job requires SQL. Not "exposure to" SQL — comfortable with joins, window functions, aggregations, and writing performant queries on large tables. If you can't write a running total with SUM() OVER (PARTITION BY ... ORDER BY ...) from scratch, practice before applying. HackerRank SQL problems at Medium difficulty are a reasonable benchmark.
Python — Specifically pandas, not NumPy exercises
You'll be assessed on data manipulation, not algorithm implementation. Being able to clean a messy CSV, merge dataframes, handle missing values, and write readable code matters more than knowing sorting algorithms. Interviewers at entry level aren't expecting you to implement gradient descent from scratch — they want to see clean, commented code that solves a real problem.
Statistics — Applied, Not Theoretical
You don't need measure theory. You do need to explain p-values correctly, know when to use a t-test versus a chi-squared test, understand confidence intervals without the "95% chance the parameter is in this range" misconception, and know what A/B test sample size means. These come up in almost every data science entry level job interview.
One Visualization Tool
Matplotlib/Seaborn for Python contexts, or Tableau/Looker for analyst-track roles. You don't need both. Knowing one well is more valuable than surface-level exposure to five.
A Portfolio With Real Data
This is where most candidates fall short. Kaggle competition scores don't impress hiring managers the way a project on real, messy public data does. Pick a dataset from a domain you find interesting — sports, music, healthcare, transportation — and publish a GitHub repo that goes from raw data to insight to clear communication. Two projects like this outperform ten Kaggle kernels.
What Actually Differentiates Hired Candidates
Among candidates who clear the technical bar, three things separate offers from rejections in data science entry level jobs:
- Communication in the technical interview: Hiring managers consistently cite "explains their thinking" as the top differentiator at entry level. Talk through what you're doing and why. Silence during a take-home review call is a red flag regardless of how good the work is.
- Domain specificity: "I want to work in data science" loses to "I want to work in data science at a fintech company because I understand credit underwriting." Specificity signals seriousness and reduces perceived risk.
- Referrals: LinkedIn's own data shows referred candidates are 4x more likely to get an offer. One informational interview with a data analyst at a target company is worth 50 cold applications.
Top Courses for Data Science Entry Level Jobs
These are courses that directly address the skill gaps that get candidates rejected, not broad "learn data science" programs that spend 40 hours on theory before touching a real dataset.
Introduction to Data Analytics (Coursera)
Rated 9.8/10. The most practical starting point for anyone targeting analyst-track entry-level roles — covers SQL, data cleaning, and stakeholder communication in a project-based format that mirrors actual job work.
Tools for Data Science (Coursera)
Rated 9.8/10. Covers the full toolchain — Python, R, Jupyter, GitHub — and is worth taking early to build the workflow habits that make your portfolio projects look professional rather than like homework assignments.
Python for Data Science, AI & Development by IBM (Coursera)
Rated 9.8/10. IBM's course is notably heavier on pandas and real data manipulation than most Python intros — which is exactly what gets tested in entry-level technical screens. Pair this with a personal project and it's a credible line on your resume.
Prepare Data for Exploration (Coursera)
Rated 9.8/10. Specifically addresses data cleaning and preparation — the work that makes up 60-80% of actual entry-level data science time but gets almost no coverage in theoretical programs.
Process Data from Dirty to Clean (Coursera)
Rated 9.8/10. A direct complement to the above. Together, these two courses build the practical data wrangling fluency that shows up in every take-home assessment at entry level.
Python Data Science (edX)
Rated 9.7/10. edX's offering goes deeper into statistical foundations than most comparable courses — worth it if your background is non-quantitative and you need to shore up the statistics fundamentals before interviews.
FAQ
Do you need a degree to get data science entry level jobs?
A computer science, statistics, or mathematics degree opens more doors, especially at large tech companies with structured hiring. But it's not a hard requirement everywhere. Financial services firms and mid-sized companies increasingly hire bootcamp grads or self-taught candidates who can demonstrate skills through a portfolio and pass a technical screen. The degree matters less the more junior the role is — at senior levels, it matters more again.
How long does it take to be ready for entry level data science roles?
With consistent daily practice, most people with a non-quantitative background get to "technically competitive" in 8–14 months. People with existing programming or statistics backgrounds can compress that to 4–6 months. The bottleneck is usually the portfolio, not the learning — building two or three projects that demonstrate real problem-solving takes time regardless of how quickly you complete courses.
What salary should I expect at entry level?
In the US, data analyst entry-level roles run $55,000–$80,000 depending on location and industry. Junior data scientist roles run $80,000–$105,000. Both figures skew higher in San Francisco, New York, and Seattle by 20–35%. Remote entry-level roles at companies headquartered in high-cost markets often pay at the top of these ranges regardless of your location.
Is it better to start as a data analyst and transition, or aim directly for data scientist roles?
For most people without a CS degree or research background, the analyst path is more reliable. You build SQL depth, stakeholder communication skills, and domain knowledge that makes you a stronger data scientist candidate 18–24 months in. Direct-to-data-scientist hiring at entry level is competitive and often filtered heavily on academic background.
What industries hire the most entry-level data science candidates?
Technology, financial services, healthcare, and retail analytics are the highest-volume sectors. Tech pays the most but is the most competitive. Financial services (banks, insurance, fintech) has high volume, structured programs, and good internal mobility. Healthcare data is growing fast and tends to be less competitive than tech for the same skill level.
Do certifications help for entry-level data science jobs?
They help as a signal of commitment, not as a credential that unlocks doors on its own. Google's Data Analytics Certificate and IBM's Data Science Professional Certificate on Coursera are recognized by recruiters as legitimate. Certifications from less-known providers are essentially invisible. The portfolio and the technical screen still matter more than any certification.
Bottom Line
Data science entry level jobs are real, but you're competing with a lot of people who took the same Kaggle courses and have the same certificate lines on their resume. The candidates who get hired are the ones who can demonstrate SQL fluency, write clean Python against real data, communicate their work clearly, and target roles that are actually entry-level rather than junior roles in disguise.
Start with one analyst-track application target, get your SQL to the point where window functions are automatic, build one project on a domain you understand, and apply to companies large enough to absorb a learning curve. That's a more reliable path than the generic "follow the full data science roadmap" advice you'll find everywhere else.