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February 8, 2026 | Data Science

Data Scientist Salary by State 2026: SF vs NYC vs Austin for SaaS Startups

Greggory Elias
By Greggory Elias
Data Scientist Salary by State

Data Scientist Salary by State 2026: SF vs NYC vs Austin for SaaS Startups

Understanding data scientist salary in the US for 2026 is the difference between burning through runway and building a sustainable data team.

Should you pay San Francisco rates for remote talent? Is Austin actually cheaper when you factor in everything? What's the real total compensation package look like—not just base salary?

These questions keep SaaS CEOs and finance teams up at night.

As we covered in our comprehensive data scientist salary guide, the headline salary number is just the beginning. Geographic arbitrage can save you $83,000 per hire annually. Or cost you that much if you get it wrong.

Here's what the data actually shows.

Data Scientist Salary by Market: 2026 Overview SAN FRANCISCO $240,000 Median Total Compensation NEW YORK CITY $195,000 Average Total Compensation AUSTIN $157,000 Mid-Level Total Compensation US NATIONAL MEDIAN $108,660 Base Salary (BLS 2024) MARKET COMPENSATION GAP 35-72% Between Major Tech Hubs AUSTIN vs SF SAVINGS $83,000 Per Hire Annually Source: Levels.fyi, BLS OEWS May 2024, 6figr.com

The Data Scientist Salary Problem in the US: 2026 Geographic Reality

The national median base salary sits at $108,660 according to BLS OEWS data from May 2024. (1)

That number is useless for hiring decisions.

Why? Because it masks a 35-72% compensation gap between major tech hubs. (2)

Mid-market SaaS companies with $10M-$250M revenue face an impossible choice:

  • Pay San Francisco rates and burn cash
  • Lowball candidates and lose every hire
  • Pick a different market entirely

Let's break down what each city actually costs.

San Francisco Data Scientist Salary: The Premium You're Really Paying

San Francisco commands the highest data scientist compensation in the United States.

The numbers:

  • Median total compensation: $240,000 for mid-level professionals (3)
  • Average base salary: $163,366 (4)
  • 75th percentile total comp: $328,000 annually (3)
  • Top 10% exceeds: $387,000 per year (3)
  • Top 1% clears: $626,000 (3)

California has 7,730 data architects employed—the highest concentration nationally. (1)

FAANG companies anchor expectations. Netflix pays data scientists over $250,000. Google, Meta, and Amazon structure packages between $220,000-$450,000 for mid-to-senior roles. (5)(6)

The purchasing power problem:

A $144,000 salary in San Francisco yields only $86,000 in actual buying power. (7)

That's a 40% reduction due to housing, taxation, and living expenses.

For SaaS companies evaluating Bay Area hires, add another 25-35% on top of salary for benefits, payroll taxes, and infrastructure support.

New York City Data Scientist Salary: The Middle-Ground Mirage

NYC positions itself between San Francisco and Austin.

The numbers:

  • Average total compensation: $195,000 (8)
  • Base salary center point: $143,251 (9)
  • Typical range: $129,531-$158,951 depending on experience (9)
  • 75th percentile: $240,000 (10)
  • Top performers exceed: $304,000 annually (10)

Manhattan-based data scientists earn slightly more at $182,118 than outer boroughs. (11)

The hidden problem:

Historical data suggests NYC data scientists were systematically underpaid relative to cost-of-living. A San Francisco-equivalent compensation would require $150,000+ to maintain comparable purchasing power. (12)

NYC might be the worst value proposition among major tech hubs when adjusted for actual living costs.

You get neither San Francisco's innovation density nor Austin's cost efficiency.

Austin Data Scientist Salary: The Value Proposition

Austin has emerged as the strategic alternative for cost-conscious SaaS companies.

The numbers:

  • Average salary: $121,630 (13)
  • Senior professional total comp: $191,000 (14)
  • Median compensation: $119,850 (1)
  • City ranks #8 nationally for data scientist employment with 2,340 professionals (1)

The tax advantage:

Texas offers zero state income tax.

That's an immediate 5-13% effective compensation savings compared to:

  • California: 13.3% top rate
  • New York: 10.9% top rate (15)

Housing costs run 50-60% lower than San Francisco.

The math that matters:

Hire three senior data scientists in Austin for the cost of two in San Francisco.

Same skill levels. Better retention rates due to improved quality-of-life factors. (12)(6)

Total Compensation Breakdown: What Data Scientist Salary Really Includes

Base salary comprises only 65-75% of total compensation packages. (1)

Here's what makes up the rest:

Total Compensation Breakdown: What You're Really Paying Components beyond base salary add 35-60% to total cost BASE SALARY 65-75% of Total Comp Entry (0-2 yrs) $80K-$105K Mid (3-5 yrs) $100K-$135K Senior (6+ yrs) $140K-$180K Principal/Staff $200K+ EQUITY 13-21% of Total Comp RSU Grants (Mid-Level) $35K-$50K/yr RSU Grants (Senior) $80K/yr Startup Equity (Series A-C) 0.1-1.5% Startup ($30M+ raised) 0-150K shares BONUSES 10-30% of Base Performance Target 15-20% Signing (Entry) $8K-$12K Signing (Senior) $20K-$25K Google L4 Signing $15K-$75K HIDDEN COSTS +15-20% Over Base Benefits Annual $12K-$20K FICA Payroll Tax +7.65% $150K Hire True Cost $172K-$180K Replacement Cost 1.5x Salary Source: BLS, Carta, JPMorgan, Boston Institute of Analytics, Abbacus Technologies

Base Salary by Experience Level

  • Entry-level (0-2 years): $80,000-$105,000 nationally (1)(16)
  • Mid-level (3-5 years): $100,000-$135,000 (1)
  • Senior (6+ years): $140,000-$180,000 base (1)
  • Principal/Staff: exceeds $200,000 (1)

Equity Compensation

Equity represents 13-21% of total compensation for mid-to-senior levels. (17)(18)

RSU grants at established companies:

  • Mid-level annual grants: $35,000-$50,000 (1)(3)(17)
  • Quarterly vesting becoming standard (19)

Stock options at startups:

  • Series A-C companies grant 0.1-1.5% equity to data scientists (20)
  • Companies with $30M+ funding: 0-150,000+ shares (20)
  • Companies under $1M funding: 0.1-1.5% equity stakes (21)

Bonuses

Performance bonuses range from 10-30% of base salary. (1)(22)

  • Mid-level target: 15-20% tied to individual and company metrics
  • Meta data scientists receive 10-30% depending on level (22)

Signing bonuses:

  • Entry-level: $8,000-$12,000 (1)
  • Senior hires: $20,000-$25,000 (1)
  • Google L4 data scientists: $15,000-$75,000 (23)

Benefits

Benefits add $12,000-$20,000 annually per employee. (1)(24)

That's 5-8% of total rewards.

A $150,000 data scientist actually costs $172,500-$180,000 when you include benefits, payroll taxes (7.65% FICA), and infrastructure. Those hidden expenses can add $123K+ to your true hiring cost.

27 Data Scientist Salary Statistics for US Hiring in 2026

National benchmarks:

  1. Median US base salary: $108,660 (BLS OEWS May 2024) (1)
  2. Employment growth projection: 36% from 2023-2033 vs 4% average across all occupations (25)
  3. Demand surge: 56% increase in job postings from 2020-2022 (26)
  4. Global positions expected to reach 11 million by 2026 (25)
  5. Salary growth: 18% annual increases for specialized roles (27)

Geographic compensation:

  1. San Francisco median total comp: $240,000 (3)
  2. San Francisco base salary premium: $163,366 average (4)
  3. NYC total compensation average: $195,000 (8)
  4. NYC base salary center: $143,251 (9)
  5. Austin average: $121,630 (13)
  6. Austin senior total comp: $191,000 (14)
  7. SF purchasing power: $144,000 salary = $86,000 actual (7)

Compensation components:

  1. Base salary proportion: 40-65% of total compensation (1)
  2. Stock/RSU annual value mid-level: $80,000 at tech companies (1)
  3. Performance bonus range: 10-30% of base (22)
  4. Signing bonus range: $8,000-$75,000 depending on level (23)(1)
  5. Benefits cost: $12,000-$20,000 annually per employee (24)
Hiring Market Dynamics: Time & Cost to Acquire Talent 2026 data shows significantly longer hiring cycles vs 2023 TIME TO HIRE MID-LEVEL 52 days SENIOR 71 days INCREASE FROM 2023 +113% Up from 31 days in 2023 HIRING COSTS & MARKET COST PER HIRE $4,700 TECH ROLE PREMIUM +$1,000 JOB GROWTH 2023-33 +36% vs 4% all occupations TECH TURNOVER 20-25% Annual attrition rate Source: InterviewPal, JoinGenius, BLS, FirstPro Inc

SaaS and startup dynamics:

  1. SaaS startup average salary: $122,833 (28)
  2. Top-of-market SaaS: $177,694 (28)
  3. Early-stage equity grants: 0.1-1.5% (20)
  4. Series A-C total packages: $220,000-$280,000 when equity valued at fundraising multiples (1)

Hiring market dynamics:

  1. Average time-to-hire for mid-level data scientists: 52 days (29)
  2. Average time-to-hire senior: 71 days (29)
  3. Time-to-hire increase: 113% up from 31 days in 2023 (30)
  4. Cost per hire: $4,700 average, add $500-$1,000 for technical roles (30)
  5. Replacement cost: 1.5x annual salary when data scientists leave (31)
  6. Tech industry turnover: 20-25% annual attrition (32)

How to Manage Data Scientist Salary Costs in the US for 2026

Hiring Strategy Cost Comparison: 6 Approaches Investment ranges sorted by minimum cost (ascending) CONTRACT / FRACTIONAL $80-$150/hr or $10K-$30K per project Savings: -20-30% vs FTE overhead JUNIOR + SR ADVISOR $115K-$170K Jr FTE + $2.5K-$5K/mo retainer Savings: -30-40% vs mid-level hire TIERED SPECIALIZATION Data Analyst $110K-$140K ML Engineer: $140K-$180K Research Scientist: $160K-$220K REMOTE-FIRST $120K-$140K Mid-level remote talent Savings: -10-15% vs on-site AUSTIN GEOGRAPHIC $120K-$190K Mid-to-senior hires Savings: -$83K/yr vs SF HYBRID EQUITY MODEL $90K-$130K Base + 0.3-0.8% equity Savings: -$20K/yr cash preserved VS. TRADITIONAL SF HIRE SF Mid-Level Total Comp $240,000 SF 75th Percentile $328,000 Source: Levels.fyi, ZipRecruiter, Comparably, Wellfound, Upwork

Geographic arbitrage through Austin:

  • Investment: $120,000-$190,000 per mid-to-senior hire
  • Savings: $83,000 annually versus San Francisco per role
  • Timeline: 3-6 months to establish presence
  • Best for: Teams of 3+ data scientists
  • Watch out for: Smaller talent pool limits senior-level options

Texas has zero state income tax. That creates an effective 5-13% compensation advantage for employees without costing you more. Central timezone facilitates collaboration with both coasts.

Hybrid compensation (higher equity, lower base):

  • Structure: $90,000-$130,000 base + 0.3-0.8% equity
  • Preserves $20,000 annual cash per hire
  • Best for: Series A-B with clear exit path in 4-6 years
  • Watch out for: Limits candidate pool to those who can accept lower cash

A mid-level data scientist receives $130,000 base instead of $150,000, paired with 0.5% equity instead of typical 0.1-0.25%. You preserve cash. They get upside alignment.

Remote-first at geographic-adjusted rates:

  • Investment: $120,000-$140,000 for mid-level remote talent
  • Remote discount: 10-15% versus on-site equivalent (33)(34)
  • Office savings: $10,000-$15,000 per employee annually
  • Best for: Companies with mature remote infrastructure
  • Watch out for: Collaboration challenges for complex analytical projects

Access national talent pools. Apply location-based compensation adjustments. Remote averages at $122,738 versus $126,554 for location-based roles.

Contract/fractional data scientists:

  • Rates: $80-$150/hour or $10,000-$30,000 per project (35)
  • No benefits overhead (saves 20-30%)
  • Best for: Early-stage with sporadic needs or specific technical challenges
  • Watch out for: Limited institutional knowledge retention after project completion

Pay only for specific deliverables. Access specialized skills like NLP or computer vision without full-time commitment. For a detailed cost comparison, see our breakdown of fractional data scientist pricing vs AI automation.

Junior talent + senior advisor structure:

  • Junior FTE: $85,000-$110,000
  • Advisor retainer: $2,500-$5,000/month
  • Total: $115,000-$170,000 versus $180,000-$220,000 for single mid-level hire
  • Best for: First data capabilities build with limited budgets
  • Watch out for: 3-6 month ramp time as juniors develop

Pair 1-2 junior data scientists with a fractional senior advisor. Senior provides strategic direction and code review. Juniors execute day-to-day work. 30-40% cost savings versus hiring experienced mid-level talent.

Tiered compensation by role specialization:

  • Data Analysts: $110,000-$140,000
  • Machine Learning Engineers: $140,000-$180,000
  • Research Scientists: $160,000-$220,000
  • Best for: Growth-stage companies with diverse data needs
  • Watch out for: Requires clarity on organizational needs upfront

Stop paying "data scientist" rates for routine analytics work. Create specialized tiers with differentiated compensation. Pay for actual skills required.

Data Scientist Salary Mistakes That Cost SaaS Companies $$$

Using national averages for local markets:

  • Cost: $25,000-$50,000 per hire in overpayment or failed recruiting
  • Fix: Segment benchmarking by specific metro area using Levels.fyi or Built In

Hiring five data scientists with 20% geographic mismatch creates $100,000-$125,000 in annual waste through overpayment. Or $125,000 in opportunity cost through failed recruiting.

Ignoring total compensation in competitive analysis:

  • Cost: $30,000-$60,000 per failed hire in lost productivity
  • Fix: Structure offers around total comp, not base salary

A $140,000 base salary appears competitive until candidates compare against $120,000 base + $40,000 equity + $18,000 bonus = $178,000 total package at competitors.

Undervaluing equity for growth-stage companies:

  • Cost: $50,000-$150,000 per hire in unnecessary cash
  • Fix: Offer 0.3-0.8% at Series B versus typical 0.05-0.1%

Many mid-market SaaS companies fail to use equity effectively. They offer 0.05-0.1% grants when competitive ranges span 0.3-0.8% for mid-level data scientists. This forces pure cash competition against tech giants.

Neglecting compensation band updates:

  • Cost: $15,000-$40,000 per employee in retention risk
  • Fix: Review bands twice annually with real-time market data

Data scientist compensation increased 18% annually in specialized roles during 2023-2024. Bands from 12 months ago undervalue talent by $15,000-$25,000.

Poor compensation communication:

  • Cost: $10,000-$30,000 in preventable negotiation escalation
  • Fix: Present total comp summary with equity scenario analysis

A $165,000 total comp package presented as "$140,000 salary" dramatically undersells the offer. Candidates decline or negotiate aggressively based on incomplete information.

Inconsistent compensation philosophy:

  • Cost: $20,000-$100,000 in internal equity issues
  • Fix: Establish written philosophy before scaling team

First data scientist joins at $110,000 pre-Series A. Second joins at $155,000 post-Series A. Both perform equally. The $45,000 gap creates resentment when discovered through peer conversations.

Data Scientist Salary US 2026 FAQs

Q: What's the real cost difference between SF and Austin for data scientists? A: $83,000 annually per mid-level hire. SF median total comp is $240,000 versus Austin at $157,000. (3)(14)

Q: How long does it take to hire a data scientist in 2026? A: 52 days for mid-level, 71 days for senior roles—up 113% from 31 days in 2023. (29)(30)

Q: Should I pay SF rates for remote data scientists? A: No. Remote positions command 10-15% lower compensation than on-site equivalents. Apply location-based adjustments. (33)(34)

Q: What percentage of total comp is equity for data scientists? A: 13-21% at mid-to-senior levels. RSU grants at established companies run $35,000-$50,000 annually. (17)(1)

Making the Right Data Scientist Salary Decision for 2026

The 35-72% compensation gap between markets isn't going away.

San Francisco pays $240,000 median. Austin pays $157,000. Same skills. Different economics.

For mid-market SaaS companies, geographic arbitrage through Austin or remote-first hiring delivers immediate savings without sacrificing talent quality.

Understanding data scientist salary in the US for 2026 means looking beyond base salary to total compensation—and beyond total compensation to actual hiring economics.

Ready to calculate what you'd save by automating your reporting instead of hiring? Get your ROI estimate here.

Sources

(1) bls.gov (2) levels.fyi (3) levels.fyi (4) builtinsf.com (5) discoverdatascience.org (6) fonzi.ai (7) lightcast.io (8) 6figr.com (9) salary.com (10) levels.fyi (11) ziprecruiter.com (12) nycdatascience.com (13) ziprecruiter.com (14) 6figr.com (15) asanify.com (16) interviewmaster.ai (17) carta.com (18) cakeequity.com (19) jpmorganworkplacesolutions.com (20) comparably.com (21) reddit.com (22) thesalarynegotiator.com (23) 6figr.com (24) bostoninstituteofanalytics.org (25) dscnextconference.com (26) imarticus.org (27) inop.ai (28) wellfound.com (29) interviewpal.com (30) joingenius.com (31) abbacustechnologies.com (32) firstproinc.com (33) ziprecruiter.com (34) ziprecruiter.com (35) upwork.com