Entry level Data Scientist salary in 2026 — TC bands and the first-job offer guide

Entry level Data Scientist salary in 2026 is a wide band because the title covers several different jobs: product analytics, experimentation, applied modeling, forecasting, decision science, and sometimes a light version of machine learning engineering. A realistic first-job offer can be anywhere from $75K at a local non-tech employer to $250K+ in year-one total compensation at a top tech company. The useful question is not "what is the average data scientist salary?" It is: what kind of data scientist offer do you have, what level is it mapped to, how much of the package is guaranteed cash, and how much leverage do you have before you sign?

Entry level Data Scientist salary in 2026: quick compensation summary

For a US-based new grad or first full-time data scientist with 0-2 years of experience, the working 2026 range looks like this:

| Employer type | Base salary | Annual bonus | Annualized equity | Typical year-one TC | |---|---:|---:|---:|---:| | Local non-tech, government, education | $70K-$98K | $0-$7K | $0-$5K | $70K-$105K | | Traditional enterprise, insurance, healthcare | $85K-$115K | $3K-$12K | $0-$15K | $90K-$135K | | Mid-market SaaS or marketplace company | $100K-$135K | $5K-$18K | $8K-$35K | $115K-$170K | | Fintech, ads, consumer tech, strong analytics org | $115K-$150K | $8K-$25K | $15K-$55K | $140K-$220K | | Big tech L3/new-grad data science | $130K-$165K | $15K-$30K | $35K-$85K | $185K-$280K | | AI lab or research-heavy applied science team | $145K-$190K | $15K-$35K | $60K-$140K | $230K-$360K |

Those are offer-pattern estimates, not a promise that every company will map to the high end. The top rows are cash-heavy and stable; the bottom rows rely heavily on equity. A $150K base with no equity may be better than a $120K base with private-company options that require years of risk. Your first filter should be guaranteed cash, then equity quality, then learning value.

What counts as entry-level data science now

Entry-level data science has become more segmented. A company may post "Data Scientist I" and actually mean one of four profiles:

• Product data scientist: SQL, experimentation, funnels, retention, pricing, dashboards, and decision support. Compensation tracks closer to product analytics.
• Decision scientist or business data scientist: forecasting, metrics, stakeholder work, light modeling, and executive reporting. Usually cash-heavy and lower equity.
• Applied data scientist: Python, modeling, causal inference, recommendations, fraud, ranking, or optimization. Pays closer to ML engineering when production impact is real.
• Research data scientist: graduate-heavy, model evaluation, econometrics, NLP, computer vision, or scientific modeling. Fewer true entry-level seats, but high offers when the bar is met.

This matters because two candidates can both have the same title and a $70K TC gap. If the role owns experiments that change revenue, it usually pays more than a dashboard role. If the role requires model deployment, feature pipelines, or production monitoring, it may be leveled like applied science. If the role is mostly reporting, it can still be a great first job, but you should not benchmark it against AI-lab packages.

Seniority bands inside the first-job market

Many first-time candidates are not identical. A bootcamp graduate, a master's student with internships, and a PhD candidate leaving academia may all be considered "entry level" by title, but not by compensation.

| Candidate profile | Realistic level | Typical TC range | Notes | |---|---|---:|---| | Career switcher with SQL portfolio, no analytics internship | Analyst/DS apprentice | $70K-$105K | Negotiate title clarity and learning path more than equity. | | Bachelor's new grad with one internship | Data Scientist I | $95K-$155K | Strong SQL and experiment examples matter. | | Master's new grad with DS internship | Data Scientist I/II | $120K-$210K | Can sometimes push above first band. | | PhD with applied modeling but limited production experience | DS II / Applied Scientist I | $150K-$260K | Level depends on business judgment, not only publications. | | New grad with top tech internship return offer | L3/L4 equivalent | $180K-$300K | Competing offers drive the top of band. |

The biggest mistake is accepting the lowest level just because the title says entry level. If you have already run experiments, shipped model-backed decisions, or owned a metric in an internship, ask whether the offer is calibrated as Data Scientist I or II. A level bump can be worth more than a small salary negotiation.

Geographic and remote adjustments

Data science compensation still follows cost-of-labor more than cost-of-living. A fully remote company may say it is location-neutral, but many quietly use bands.

• Tier 1 markets: San Francisco Bay Area, New York, Seattle, and sometimes Los Angeles. Add 10-25% to base versus a national band, and expect more equity at tech companies.
• Tier 2 markets: Austin, Boston, Denver, DC, Chicago, Atlanta, Raleigh, and Portland. Usually 85-95% of Tier 1 cash, with similar title expectations.
• Lower-cost US markets: Often 70-85% of Tier 1 base. Remote-first startups may be higher if they compete nationally.
• Canada and Europe: Cash is often meaningfully lower than US packages, but benefits, vacation, and work authorization support may matter more for a first role.

For a first data science job, remote can be a tradeoff. Remote gives access to national pay bands, but junior data scientists often learn faster near product managers, engineers, and senior analysts. If the remote offer is much higher, take it seriously. If the difference is small, the better mentorship environment may be worth more than a few thousand dollars.

What moves the offer

Entry-level negotiation is real, but the levers are different from senior negotiation. Recruiters rarely have unlimited room for a first-job candidate. They do have enough room to improve an offer when the ask is specific and justified.

1. Competing offers: The cleanest lever. A credible written offer with a higher base or TC can move base by $5K-$15K and equity/sign-on by $10K-$40K.
2. Leveling evidence: Internship impact, shipped analyses, statistical depth, or a graduate degree can support a DS II calibration.
3. Team scarcity: Fraud, pricing, marketplace dynamics, ads measurement, and experimentation-heavy teams tend to have more comp flexibility than generic reporting teams.
4. Start date and location: Earlier start dates, relocation, or willingness to work hybrid in a hard-to-fill office can unlock sign-on money.
5. Equity risk: If private options are a big part of TC, ask for more cash or a larger grant. Do not treat private equity at face value.

The least effective lever is "I found a higher average online." The most effective lever is "I am excited about the role, but I have another offer at $X base and $Y TC. If you can get this to $Z base or add a $A sign-on, I would be ready to sign."

Negotiation anchors for a first data scientist offer

Use ranges that fit the employer type. If a regional healthcare company offers $92K base, asking for $170K will make you look uncalibrated. Asking for $102K and a $5K sign-on is reasonable. If a fintech offers $125K base and $20K annual equity, asking for $135K-$140K base or an equity/sign-on bump is reasonable. If big tech offers $185K TC, the right ask may be $210K-$230K TC with the increase concentrated in equity and sign-on.

A practical script:

I am very excited about the team and I can see myself accepting. Before I do, I wanted to ask whether there is room to improve the package. Based on the scope of the role and another process I am in, I was hoping to get closer to $___ base / $___ year-one total compensation. If we can bridge that gap through base, sign-on, or equity, I would be comfortable moving forward.

Do not bluff. Do not invent competing offers. Do not say you will sign if you are not ready. In a small market, recruiter trust follows you longer than you think.

Startups vs big tech for entry-level data scientists

Big tech usually wins on compensation, brand, structured mentorship, and transferability. The downside is narrower ownership: you may spend months on one metric, one experiment framework, or one product surface. Startups usually win on scope and speed. You may sit with the founder, build the first retention model, and change product decisions quickly. The downside is messy data, fewer senior reviewers, and equity that may never be liquid.

For a first role, the best startup offer has three traits: a senior data leader who will review your work, a product team that actually uses analysis, and equity explained in percentage terms plus strike price. If a startup will not tell you the option count, fully diluted shares, strike price, and vesting schedule, do not count the equity as real TC.

Mistakes to avoid

• Optimizing only for title. "Data Scientist" at a weak team may teach less than "Product Analyst" at a strong experimentation org.
• Comparing private options to public RSUs one-for-one. A $40K annual RSU grant at a public company is not the same as $40K of private options.
• Ignoring base salary because the company says the opportunity is high growth. Rent is paid with cash.
• Taking a remote junior role with no senior DS support unless you are unusually self-directed.
• Forgetting immigration, relocation, and bonus clawback details. They can change the real value of the offer.

FAQ

What is a good entry-level data scientist salary in 2026? A good US offer is usually $100K-$140K base and $115K-$180K total compensation. Big tech and top fintech can be meaningfully higher.

Can a new data scientist negotiate? Yes, especially with another offer. Aim for a targeted $5K-$15K base increase, sign-on, relocation, or equity improvement rather than a vague request.

Is a master's degree worth more money? Sometimes. It helps most when paired with internships, experimentation skill, causal inference, or applied modeling. A degree alone rarely guarantees a higher band.

Should I choose a lower salary for better mentorship? Often, yes, if the gap is modest. The first 18 months set your habits, portfolio, and level trajectory. But if the pay gap is $50K+ and the higher-paying team has decent senior support, take the money seriously.

Sources and further reading

Compensation data shifts quickly. Verify any specific number against the latest crowdsourced postings before relying on it for negotiation.

• Levels.fyi — Real-time tech compensation data crowdsourced from candidates and recent offers, with company- and level-specific breakdowns
• Glassdoor Salaries — Self-reported base salaries across companies, roles, and locations
• Bureau of Labor Statistics OES — Official US Occupational Employment and Wage Statistics, useful for non-tech baselines and metro-level comparisons
• H1B Salary Database — Public H-1B salary disclosures, useful as a lower-bound for what large employers will pay sponsored candidates
• Blind by Teamblind — Anonymous compensation discussions, often surfaces refresh and bonus details Levels misses

Numbers in this guide reflect publicly available data as of 2026 and should be cross-checked against current postings before negotiating.

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