Data Scientist Salary

Bureau of Labor Statistics title: Data Scientists

The median data scientist salary in the United States is $120,230 per year ($57.80/hour). Salaries range from $67,240 at the entry level to $199,130+ for the top 10% of earners. There are 262,440 data scientist jobs nationwide. Employment is projected to grow 33.5% through 2034.

Last updated: June 2026 Β· Source: U.S. Bureau of Labor Statistics

Also known asData AnalystML EngineerMachine Learning EngineerAI EngineerResearch Scientist
Median Salary
$120,230
per year
Hourly Rate
$57.80
per hour
Employment
262,440
jobs in the U.S.

Salary Distribution

10th percentile
$67,240
25th percentile
$85,660
Median
$120,230
75th percentile
$158,880
90th percentile
$199,130

Job Growth (2024-2034)

+33.5%
Much faster than average

Annual Openings

23,400
jobs/year (growth + replacements)

Education Required

Bachelor's degree

Data Scientist Career Guide

Interview questions, resume templates, and a career ladder written specifically for data scientists.

Data scientists build models that turn business questions into predictions and drive product decisions. The role splits into three tracks with very different compensation: (1) product/analytics DS (partnering with product managers, SQL-heavy, causal inference), (2) ML engineering (production models, deployment, feature engineering, platform), (3) research/applied science (papers, novel methods, PhD-heavy). Total comp varies 3x between traditional enterprise DS and FAANG ML engineer. Employers screen for: SQL fluency, statistical rigor, ML fundamentals, ability to translate business questions into models, and communication with non-technical stakeholders.

Core Skills Hiring Managers Screen For

SQL mastery
Window functions, CTEs, complex joins. Product DS interviews often 60% SQL. Non-negotiable.
Python / R + core ML libraries
pandas, scikit-learn, numpy. PyTorch or TensorFlow for deep learning. Comfort building an end-to-end model in a Jupyter notebook.
Statistics + experimentation
A/B testing, hypothesis testing, power analysis, causal inference (diff-in-diff, IV, propensity score). Product DS screens hard here.
ML fundamentals
Regularization, cross-validation, bias/variance, common failure modes. Deep learning if applying for ML engineer roles.
Feature engineering
Turning raw data into signals a model can learn from. Often more impactful than model choice.
Communication
Translating a model into a business decision without jargon. Presenting to non-technical stakeholders is weighted heavily at senior+.
Production / MLOps (ML engineer track)
Model deployment, monitoring, drift detection, feature stores, model serving.

How to Become a Data Scientist

  1. 1
    Bachelor's in quant field (stats, CS, econ, physics)
    Master's or PhD helps for research/applied science tracks. Not required for product DS.
  2. 2
    Build 2-3 real projects
    End-to-end: data β†’ cleaning β†’ model β†’ deployment (or clear results). Kaggle competitions count but real business problems count more.
  3. 3
    Land first role β€” usually analytics before DS
    Business analyst or analytics engineer for 1-2 years is a common on-ramp. Or new-grad DS at large companies with structured programs.
  4. 4
    Specialize by year 3
    Product DS, ML engineer, applied research, or MLOps. Each has different career trajectories and pay bands.
  5. 5
    Move to a high-paying employer
    FAANG ML pays 2-3x traditional enterprise. Once you have 2-3 years, the pay bump for moving to Big Tech is enormous.

Career Progression

Data Analyst / Junior DS
0-2 yrs$75k-$110k Β· Big Tech $130-170k TC
  • β€’Ad-hoc analysis for PMs and business stakeholders
  • β€’Build dashboards and metrics
  • β€’Simple predictive models under guidance
Next: Data Scientist / Senior Analyst.
Data Scientist / ML Engineer II
2-5 yrs$110k-$160k Β· Big Tech $200-320k TC
  • β€’Own end-to-end projects
  • β€’Design A/B tests
  • β€’Build production models with team support
Next: Senior DS / L5 MLE.
Senior Data Scientist
5-8 yrs$160k-$230k Β· Big Tech $300-500k TC
  • β€’Own strategy for a product area or model family
  • β€’Mentor junior DS
  • β€’Interface with senior product / eng leadership
Next: Staff or Manager.
Staff / Principal DS
8+ yrs$230k-$350k Β· Big Tech $450k-$800k+
  • β€’Cross-team technical strategy
  • β€’Own org-wide ML initiatives
  • β€’Represent DS in exec planning
DS Manager / Director
6+ (parallel) yrs$200k-$300k Β· Big Tech $400-700k+
  • β€’Manage 5-15 DS + MLE
  • β€’Own team roadmap and business metrics
  • β€’Hiring + calibration

Interview Questions & Answers

5 free questions below. Career Kit unlocks 15 total plus resume bullets for every level.

Q1. Design an A/B test for a new feature that has 5% expected lift.technical
STAR + stats. Define primary metric + guardrail metrics. Compute sample size for 80% power at Ξ±=0.05 using expected effect + baseline variance. Determine test duration based on daily traffic. Randomization unit (user, session, cluster). Analysis plan pre-committed. Watch for peeking, novelty effects, network effects. If practical significance β‰  statistical significance, discuss decision criteria.
Q2. How do you handle missing data?technical
Depends on why. MCAR (missing completely at random) β€” can drop or impute with mean/median. MAR (missing at random given observed) β€” impute conditional on other features, or use models that handle missingness natively. MNAR (informative missingness) β€” often needs domain-specific handling; sometimes missingness itself is a feature. Never blindly fillna(0).
Q3. Walk through a project you're proud of.behavioral
STAR. Business problem β†’ approach β†’ key technical decisions β†’ results with numbers β†’ what you'd do differently. Interviewers screen for whether you can articulate impact, not just method choice.
Q4. How would you predict customer churn?technical
Frame: what's the business action, what's the prediction window? Data pipeline: user features (usage, tenure, demographics), engagement features (recency, frequency), historical churn labels. Model: start with logistic regression as baseline, try gradient boosting (XGBoost, LightGBM). Metrics: AUC-PR for imbalanced classes, calibration for decision thresholds. Watch for label leakage (feature computed at time of churn). Deploy with monitoring.
Q5. Why data science over software engineering?culture
Concrete. 'I like the ambiguity β€” you don't always know the answer at the start.' Or 'business impact through analysis rather than code shipped.' Or 'I did a research project in college and it hooked me.' Not generic.

Resume Template β€” Data Analyst / Junior DS (0-2 yrs)

Copy these bullets into your resume as a starting point. Career Kit includes bullets for every level of the ladder.

Data Analyst / Junior DS (0-2 yrs)
  • β–ΈBuilt churn prediction model for consumer subscription product, identifying 40k at-risk users monthly and driving 12% reduction in monthly churn through targeted retention campaigns.
  • β–ΈDesigned and ran A/B test for onboarding flow redesign; result drove 8.2% lift in day-7 activation, adopted as new default.
  • β–ΈAutomated weekly executive KPI report from 6 hours of manual work to 15 minutes via SQL + dbt pipeline.
  • β–ΈMentored 2 summer interns through their first production analyses; both received return offers.

What Gets You Rejected

  • βœ—Can't write a window function on the whiteboard. Immediate DQ at analytics-heavy roles.
  • βœ—Framing a project without stating the business impact. 'I built a model that got 85% AUC' with no context signals junior thinking.
  • βœ—Overselling β€” claiming you 'productionized' something you handed off to an ML engineer.
  • βœ—Not knowing common ML failure modes (overfitting, target leakage, distribution shift).
  • βœ—Vague on statistics β€” you'll be tested on p-values, power, hypothesis tests.
  • βœ—Kaggle-only experience without real business projects.

Salary Negotiation Levers

  • βœ“Competing offers β€” the single biggest lever, especially between FAANG and mid-tier.
  • βœ“Specialty (NLP, computer vision, RL, causal inference) β€” commands premium at companies solving those problems.
  • βœ“PhD in relevant field β€” worth $30-80k at applied research roles.
  • βœ“Publications or notable open-source contributions.
  • βœ“Sign-on bonus and equity refresh (year 2, not year 4) β€” often more negotiable than base.
  • βœ“Level, not just pay β€” L4 vs L5 at FAANG is worth $80-150k in TC.

Data Scientist Resume Review β€” $29

Personalized resume review from someone who has hired data scientists. Purchase, email your resume to support@weblabra.com, and receive detailed written feedback within 7 days.

  • βœ“ Occupation-specific critique (not generic)
  • βœ“ Rewrite suggestions for weak bullets
  • βœ“ Missing skills / keywords flagged
  • βœ“ Formatting + ATS-readability feedback
  • βœ“ 7-day turnaround, 100% refund if we miss it

Data Scientist Career Kit β€” $19

  • βœ“ All 15 interview questions with expert answers
  • βœ“ 10 real questions aggregated from Glassdoor, Blind, Reddit, LinkedIn
  • βœ“ Interview format intel for 6 top employers
  • βœ“ 3 copy-paste salary negotiation emails
  • βœ“ Resume bullets for every level of the career ladder
  • βœ“ 30–60–90 day plan for your first role
  • βœ“ Cover letter templates specific to this occupation
  • βœ“ Detailed salary negotiation script
  • βœ“ Ranked list of certifications with cost + ROI
  • βœ“ Downloadable PDF for offline access
  • βœ“ Instant access, no expiration, no subscription
βœ“ 7-day money-back guarantee β€” no questions asked

One-time purchase. 7-day refund guarantee.

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Data Scientist Salary by State

Best Cities (cost-adjusted) β†’

Salary Map β€” Click a state for details

$69K
$163Kβ–  No data

Top Paying States

Washington
$163K
+36%
California
$142K
+18%
Maryland
$136K
+13%
New Jersey
$135K
+13%
Massachusetts
$132K
+10%
New York
$130K
+9%
Minnesota
$129K
+7%
Vermont
$127K
+6%
District of Columbia
$126K
+5%
Virginia
$126K
+5%
Vertical line = national median. Click a state for full details.
StateMedianvs. National
Washington$163,350+35.9%
California$141,590+17.8%
Maryland$136,370+13.4%
New Jersey$135,280+12.5%
Massachusetts$131,750+9.6%
New York$130,460+8.5%
Minnesota$128,800+7.1%
Vermont$127,070+5.7%
District of Columbia$126,490+5.2%
Virginia$126,430+5.2%
1–10 of 49

Data Scientist Salary by Metro Area

Metro AreaMedianvs. National
San Jose-Sunnyvale-Santa Clara, CA$185,080+53.9%
San Francisco-Oakland-Fremont, CA$170,110+41.5%
Idaho Falls, ID$167,840+39.6%
Seattle-Tacoma-Bellevue, WA$164,740+37.0%
Kennewick-Richland, WA$156,830+30.4%
Charlottesville, VA$149,800+24.6%
North Port-Bradenton-Sarasota, FL$146,980+22.2%
Bridgeport-Stamford-Danbury, CT$141,760+17.9%
Corvallis, OR$140,190+16.6%
New York-Newark-Jersey City, NY-NJ$135,980+13.1%
1–10 of 283
Data Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics (OEWS). Data reflects the most recent annual survey.
How does this compare to pro athletes?
A data scientist earning $120,230/year would need 496 years to match Stephen Curry's NBA salary, or 532 yearsto match Patrick Mahomes' NFL contract.
See all sports & celebrity salaries β†’
πŸ“ˆ See how data scientist salaries have changed over the past decade.
View Data Scientist salary trend (2011–2024) β†’