How to Get an Entry-Level Data Science Job (With No Experience)

"Entry-level, 1-2 years of experience required." If you've been job-hunting in data science for more than a week, you've seen this exact contradiction on a dozen postings, and it's genuinely one of the most demoralizing patterns in the whole search. Here's the useful part: that contradiction is mostly a symptom of how a small number of large companies write postings, not a universal rule, and knowing which titles, company sizes, and skills actually match a true first data science job changes the search completely.

The data scientist field added roughly 23,400 openings a year on average over the current decade, according to the Bureau of Labor Statistics, and a meaningful share of those are genuinely junior roles at companies too small to run a formal new-grad pipeline. The jobs exist. This guide is about finding the ones that actually match "entry-level" and giving yourself a realistic shot at them.

The Short Answer: How to Land an Entry-Level Data Science Job

1. Understand what "entry-level" really means in this field: 0-2 years of relevant experience, not zero skills.
2. Target the right job titles, since "junior data analyst" and "data science intern" are far more attainable first roles than "data scientist" at a large company.
3. Know what entry-level postings actually screen for: SQL, basic statistics, and clear communication, more than advanced machine learning.
4. Build 2-3 right-sized projects that prove baseline competence, not a research-grade thesis.
5. Target smaller companies and teams, where the real entry bar is usually lower than the job title suggests.
6. Use internships and junior-track programs as a deliberate way in, not a fallback.
7. Search and filter specifically for junior/entry-level roles, rather than applying broadly across every seniority level and hoping.

What Actually Counts as "Entry-Level" in Data Science?

An entry-level data science job is a role requiring roughly 0-2 years of relevant, paid or unpaid experience, aimed at someone who can execute defined analytical or modeling tasks under supervision rather than independently own a project end to end. That's a meaningfully different bar than "zero skills," and it's also a meaningfully lower bar than the "2+ years required" language on a lot of postings implies.

The confusion happens because two different things get labeled "entry-level" in job postings: roles genuinely designed for first-time data professionals (internships, junior analyst roles, some junior data scientist roles at smaller companies), and roles that use "entry-level" loosely to mean "not a senior/staff position" while still expecting 1-2 years of specific tool experience. Learning to tell these apart from the posting itself, covered in Step 5 below, saves a lot of wasted applications.

A few quick signals that a posting is a genuine first-job fit rather than the second kind: it lists a specific, narrow tool set instead of a dozen technologies; it describes the role as supporting a senior analyst or data scientist rather than owning a workstream independently; and the years-of-experience line says "0-2" or "1-2 preferred" rather than a flat "3+ years." None of these signals are perfectly reliable alone, but together they're a fast way to triage a list of postings before spending real time tailoring an application to each one.

The Entry-Level Data Science Job Titles You Should Actually Be Targeting

Most career advice on this topic lists generic titles without much detail on how attainable each one actually is for a first-time applicant. Here's a more specific breakdown, using the same role categories a real job board (this one included) uses to classify actual live listings:

Bottom line: if you're optimizing for your realistic first job, data analyst and junior data scientist roles at small-to-mid-size companies are where most successful first-time applicants actually land, not the "data scientist" title at a company with a formal new-grad program.

Where each title tends to live on a job board also matters practically. Data analyst and analytics engineer postings appear across nearly every industry, including many that don't think of themselves as "tech" companies at all (retail, healthcare, logistics), which meaningfully widens the pool of realistic applications beyond the tech-company postings most job seekers default to searching. Junior data scientist and AI engineer postings skew more toward tech, fintech, and increasingly toward companies building products directly on top of large language models, a category that's grown quickly enough that it's worth checking specifically rather than assuming it only exists at a handful of well-known AI labs.

What Do Entry-Level Data Science Jobs Pay?

The field-wide median for data scientists in the US was $112,590 as of May 2024, according to the BLS, but that figure blends junior and senior compensation across the whole occupation, so it overstates what a first role typically pays. BLS doesn't publish an entry-level-specific breakout for this exact title, and a lot of the entry-level-specific numbers circulating online are self-reported and vary enormously by company size, location, and the specific title from the table above (a junior data analyst role and a junior data scientist role at the same company can differ by tens of thousands of dollars). Rather than repeat an unverified number here, the honest guidance is this: expect a first role to pay meaningfully below that field-wide median, expect data analyst titles to trend lower than data scientist titles for a comparable company size, and check the specific posting's listed range (a growing number of US states now require it by law) rather than a national blog average.

Do You Need a Degree for an Entry-Level Role?

Not always, though it depends heavily on which title from the table above you're targeting and how you compensate for its absence. Data analyst and analytics engineer roles are the most commonly attainable without a four-year degree, provided you can demonstrate SQL and reporting skills directly. Junior data scientist roles at larger, more selective companies are the hardest to land without one. We cover this specific question, including which companies are realistically most flexible on the degree requirement, in full in Can You Get a Data Science Job Without a Degree, Certificate, or Experience?.

Step 1: Build the Skills Entry-Level Postings Actually Screen For

Entry-level screens are narrower than the wall of tools in the job description suggests:

1. SQL, close to universally, and usually tested directly in the first technical round regardless of the specific title.
2. Basic statistics: descriptive stats, simple hypothesis testing, and the ability to sanity-check a number before presenting it, well ahead of anything approaching advanced inferential statistics.
3. Python or R fundamentals: data cleaning and manipulation with pandas or dplyr, not custom model architectures.
4. One BI or visualization tool (Tableau, Looker, Power BI), which shows up constantly in data analyst and junior data scientist postings alike.
5. Clear written and verbal communication, tested more than most candidates expect, often through a short presentation or written summary as part of the interview process.

Notice what's not on this list at the entry level: deep learning, big data infrastructure (Spark, Hadoop), and advanced statistical modeling. These show up in job descriptions as "nice to have" far more often than they show up as an actual screening bar for a first role, and spending your limited prep time here instead of on SQL is a common, avoidable mistake.

A useful way to check your own prep against this list: pull three or four real entry-level postings you'd actually want, and count how many times SQL, a specific BI tool, and basic statistics come up versus how many times deep learning frameworks or distributed computing tools come up. In almost every case, the fundamentals list wins by a wide margin, and that ratio is a better guide to where to spend your next few weeks of studying than a generic "skills every data scientist needs" list aimed at the whole field, senior roles included.

Pro Tip: If a posting lists ten or more tools, treat the first three or four as the real requirements and the rest as a wish list. Entry-level interviews rarely test more than a handful of tools in any depth, regardless of how long the posting's tool list runs.

Step 2: Build 2-3 Projects Sized for a First Job, Not a Senior One

The full portfolio playbook (what makes a project actually convincing, how to structure a README, how to deploy something simple) is covered in depth in the Career Entry guide. The entry-level-specific note worth adding here: don't over-scope your projects trying to prove senior-level ability. A hiring manager screening for a junior role isn't looking for a novel modeling technique; they're looking for evidence you can clean real data, choose a reasonable and explainable approach, and communicate the result clearly. Two well-executed, appropriately-scoped projects beat one overly ambitious project that's 80% finished or barely explained.

Step 3: Target the Right Company Size, Not Just the Right Title

This is the step most competitor guides skip, and it matters more than almost anything else in this list. A "data scientist" posting at a company with 30,000 employees and a formal new-grad recruiting pipeline is competing against candidates from target schools with multiple internships already completed. The identical title at a 40-person startup, or a mid-size company hiring its first or second data person, is often a genuinely different, more attainable bar, because the company needs someone productive quickly and has less rigid pedigree filtering built into its process.

Practically: don't rule out a "data scientist" title just because a big company's version of that role feels out of reach. Look at company size and how many data roles the company already has. A company hiring its first data hire is looking for a different profile than one filling its fortieth data science seat.

There's a real tradeoff here worth naming honestly. A first data hire at a small company often means less structure, less mentorship, and more ambiguity about what "doing the job well" even looks like, since there's no established team to learn from. A large company's fortieth data science seat comes with more mentorship and clearer expectations, at the cost of a much higher bar to get in the door. Neither is objectively better as a first job; the right call depends on whether you value structured learning or you value simply getting real, unsupervised reps as fast as possible. Many successful data scientists take the smaller-company route specifically for the second reason, then move to a larger, more structured company for their second role once they have something concrete to show for it.

A practical way to gauge company size and data-team maturity before applying: check how many people on LinkedIn currently hold a data-related title at the company, look at how long the specific job posting has been open (a posting open for months at a small company sometimes signals a genuinely hard-to-fill junior seat, not just high standards), and read the job description's tone. A posting that reads like a wish list for a unicorn candidate usually comes from a company that hasn't hired for the role before and doesn't yet have a realistic sense of the market; a posting that reads like a specific, bounded set of responsibilities usually comes from a company that knows exactly what it needs.

Step 4: Use Internships, Apprenticeships, and Junior-Track Programs

If you're earlier in this process, structured entry paths reduce a lot of the "how do I get experience without experience" problem directly:

1. Internships, including for career changers, not just current students. Several companies now run "returnship" or adult-internship style programs specifically for career switchers.
2. Apprenticeship and fellowship programs aimed at data roles, which typically combine paid training with a real project and a placement track.
3. Contract-to-hire roles, which are often listed with a lower bar than a direct-hire role because the company is hedging its own risk, and which convert to full-time at a meaningfully higher rate than a cold application does.
4. Rotational analyst programs at larger companies, which sometimes have a lower initial bar than a directly-titled "data scientist" role and offer a path to a data-focused rotation later.
5. Alumni and bootcamp career-services placements, if you went through a formal program. These pipelines exist specifically because employers trust the program's screening, and skipping this resource in favor of only applying cold leaves a real advantage on the table.

None of these paths are a lesser route into the field. A meaningful share of working data scientists and analysts started in exactly one of these programs rather than being hired directly into a permanent role, and naming the specific program on a resume ("Data Analytics Apprenticeship, Cohort 4") reads as a stronger, more concrete signal than a vague "self-taught, various online courses" line.

Step 5: Search and Filter Specifically for Entry-Level and Junior Roles

A generic search for "data scientist jobs" buries true entry-level postings under senior and staff-level listings, since most general boards don't let you filter reliably by real seniority. This is exactly the problem a seniority-tagged filter solves: on finddatasciencejobs.com, every listing is tagged junior, mid, senior, or staff at the point it's added, so filtering to junior-only cuts out the noise a plain keyword search leaves in. Whichever board you use, look specifically for:

1. A seniority filter, used deliberately, rather than scanning titles by eye.
2. Listed years-of-experience ranges of 0-2, treating "0-3" language with some caution since it often skews toward the higher end in practice.
3. Internship and new-grad-specific sections, which some boards and most large companies maintain separately from their general listings.
4. Company size as a filter or a manual check, per Step 3, since it predicts the real bar better than the title alone.

How Long Does It Actually Take to Land Your First Role?

Once your skills and portfolio are genuinely ready, most focused entry-level searches take 2 to 5 months to produce an offer, assuming consistent weekly applications targeted at the right titles and company sizes from the steps above. That's faster than the 4-9 month range in the broader Career Entry guide, mainly because "entry-level" candidates who target the right smaller companies and titles face less competition per posting than candidates applying broadly to every "data scientist" listing regardless of seniority. The range widens considerably for candidates still building core skills at the same time as applying; in that case, expect the timeline from the pillar guide instead, since the job search hasn't really started until the skills are in place.

Mistakes That Keep Candidates Stuck in "Entry-Level Purgatory"

1. Applying only to "data scientist" titles at large, well-known companies, where the real applicant pool skews toward candidates with internships already completed. It feels like the most prestigious path, but it's frequently the slowest one for a genuine first job.
2. Treating every "entry-level, 1-2 years required" posting as off-limits, when many are worth applying to anyway, particularly at smaller companies, since posted requirements are frequently a wish list rather than a hard filter. A rule of thumb: if you meet roughly 60-70% of the listed requirements for an entry-level posting specifically, it's worth applying.
3. Ignoring data analyst and analytics engineer titles in favor of chasing the "data scientist" label specifically, even when analyst roles are the faster, more realistic way into the field for most first-time candidates. The title on year one rarely matches the title on year three.
4. Over-building portfolio projects aimed at a level of complexity no one is screening for at the entry level, instead of two clean, well-explained, appropriately scoped ones. A polished, clearly-explained project that solves a modest problem well outperforms an ambitious one that's rushed or hard to follow.
5. Ignoring company size as a signal, and applying with the same strategy, timeline expectation, and resume framing to a 40-person startup and a 40,000-person enterprise, when the two require genuinely different approaches per Step 3.
6. Not tracking which applications get responses. Without at least a simple log of which titles, company sizes, and resume versions get callbacks versus silence, it's hard to tell whether the strategy needs adjusting or the search just needs more time.

FAQ

Is data scientist a good entry-level job title to target?

It can be, but it's the hardest of the common entry-level titles to land at large, competitive companies. Data analyst and analytics engineer titles are generally more attainable as a genuine first role, and both are common stepping stones into a data scientist title 1-2 years later. Targeting "data scientist" specifically at smaller companies, rather than large ones, meaningfully improves the odds.

How many years of experience actually counts as "entry-level" in data science?

Roughly 0-2 years of relevant experience, including internships, freelance work, and substantial personal or academic projects, not strictly paid full-time work. Postings listing "1-2 years required" for an entry-level title are frequently flexible on this in practice, especially at smaller companies, and are worth applying to rather than automatically skipping.

Can you start as a data analyst and move into data science later?

Yes, and it's one of the most common and realistic paths into the field. A data analyst role builds the SQL, business context, and stakeholder communication skills that data science interviews test directly, and an internal transfer or lateral move into a data scientist title after 1-2 years is a well-worn path at many companies.

What GPA or coursework matters for an entry-level data science role?

Less than most candidates assume outside of formal campus recruiting pipelines at large companies. Mid-size companies and startups weigh a demonstrated portfolio and SQL/statistics competence far more heavily than a specific GPA threshold. Where GPA does matter is almost exclusively in structured new-grad programs at large companies that use it as an initial screening filter before a human ever reviews the application.

Are entry-level data science jobs available remote?

Yes, though they're generally more competitive than an equivalent on-site posting, since a remote entry-level listing draws applicants nationally rather than from one metro area. We cover remote-specific search strategy in Are Data Science Jobs Remote? How to Land a Remote Data Science Role .

Your Next Step

If you take one thing from this guide, take Step 3: stop treating every "data scientist" posting at a large company as your primary target, and start looking at data analyst and analytics engineer titles at smaller companies as the faster, more realistic way in. The title on your first offer doesn't lock in your career path. It gets you the experience that makes the next move easier.

Browse current entry-level and junior data science, analyst, and ML roles, filterable by seniority and role type, on finddatasciencejobs.com.

Read next:

• How to Get a Job in Data Science: The Complete 2026 Guide
• Can You Get a Data Science Job Without a Degree, Certificate, or Experience?
• Are Data Science Jobs Remote? How to Land a Remote Data Science Role