Data Scientist Salary in Washington DC — 2026 BLS Data
Salary distribution
Percentile breakdown of Data Scientist base salaries in Washington DC.
Washington DC’s median base salary for a data scientist is $137,120, according to BLS OEWS May 2024 data (SOC 15-2051) for the Washington-Arlington-Alexandria, DC-VA-MD-WV metropolitan area. That puts DC roughly 22% above the national median of $112,590 — a meaningful premium, but one that tells only part of the story. The market here is structurally bifurcated in a way no other major metro is: federal government and defense contractor roles coexist with private-sector tech, finance, and consulting employers, and those two segments price talent very differently. Where you land within that range matters far more than the headline median.
What the median hides
The $137,120 median covers everyone from a GS-13 analyst at a federal agency grinding through clearance paperwork to a senior ML engineer at a D.C.-based fintech company working with production recommendation systems. That is not a homogeneous population, and the spread reflects it.
The 25th percentile sits at $102,440. These are typically early-career data scientists in government or nonprofit roles, people carrying the title with SQL and basic statistical modeling but limited ML depth, or those working in smaller consulting shops that serve agencies on thin margins. The 75th percentile lands around $172,000 — mid-to-senior practitioners at private employers, defense contractors with cleared work, or staff-level roles at financial services firms. The 90th percentile reaches $208,600, which is where you find principal-level ICs at Capital One, Booz Allen Hamilton’s elite analytics practices, or ML engineers pulling double-duty at emerging AI consultancies.
The BLS 90th-percentile figure is top-coded, meaning wages above a threshold are reported at the cap rather than their true value. For data scientists at senior levels in highly specialized cleared or finance-adjacent roles, actual base pay can run higher still. Treat $208,600 as a floor for the top decile, not its ceiling.
How DC compares to other major hubs
Washington DC is not a tier-1 tech hub in the way that San Francisco, Seattle, or New York is, and the salary data reflects that. The national data-scientist median is $112,590. DC’s median of $137,120 beats that convincingly, driven by the high concentration of government-adjacent employers that pay competitive wages to attract talent with clearances or specialized domain knowledge.
San Francisco’s comparable figures run considerably higher — senior data scientists at Bay Area tech firms commonly earn $180K-$230K base. Seattle, anchored by Amazon, Microsoft, and a dense cluster of AI-focused teams, sits in the $160K-$200K range for mid-to-senior talent. New York’s finance sector pushes data scientists at hedge funds and investment banks past $200K base at senior levels.
DC’s advantage over those markets is not salary — it is stability and sector diversity. Federal agencies, defense primes (Booz Allen, Leidos, SAIC, Palantir), civilian contractors, policy-focused think tanks, and a growing fintech cluster around Capital One and GEICO together create a thick job market that rarely dries up in downturns. During the 2022-2023 tech layoff cycle that hammered private-sector data science hiring in SF and Seattle, DC’s government-adjacent segment kept hiring steadily because agencies operate on multi-year contract vehicles, not quarterly earnings pressures.
What drives the spread: employer tier, level, and specialty
Three variables explain most of the P25-to-P90 disparity in DC.
Employer tier. Federal government direct hires (GS pay scale) typically land data scientists at GS-13 to GS-14, which in the DC locality pay area means $117,962-$153,354 for 2024 (OPM locality pay data). That range covers a lot of the lower two-thirds of the BLS distribution. Defense contractors and management consulting firms (Deloitte, McKinsey, Accenture Federal Services) pay a layer above that — typically $130K-$175K for similar experience levels, in part to compensate for the lack of federal benefits. Private-sector employers — Capital One, the Federal Reserve Board, fintech startups, and the handful of pure-play tech companies with DC footprints — pay at or above the private-sector national norm, often $150K-$210K+ for mid-to-senior roles.
Security clearance. This is the DC-specific variable that exists nowhere else at the same scale. Holding an active TS/SCI clearance typically commands a 15-25% salary premium over equivalent uncleared roles. The premium reflects the cleared talent pool’s scarcity and the time cost (often 12-24 months) for an employer to sponsor a new clearance. A data scientist with TS/SCI and ML skills working on intelligence community contracts sits firmly in the 75th-to-90th-percentile bracket even at mid-career experience levels.
Technical specialty. Roles requiring deep ML engineering, NLP, computer vision, or large language model deployment pay 20-35% more than general analytics or business intelligence roles carrying the same “data scientist” job title. The DC market has significant demand for geospatial data scientists (satellite imagery, OSINT), NLP specialists (document processing for federal agencies), and applied ML engineers who can deploy models in classified or air-gapped environments. These niches are chronically under-supplied and price accordingly.
Total compensation breakdown
BLS tracks base salary only. For a data scientist at the DC median, the realistic total-comp picture looks like this:
- Base: $137,120. The BLS-reported median. For government employees this is essentially all cash compensation. For contractors and private-sector roles, it is the largest component.
- Annual bonus: ~$14,000. Private-sector employers typically offer 8-15% annual cash bonuses. Federal employees and most government contractors receive no performance bonus in the private-sector sense (though some contractors receive end-of-year spot bonuses). Weighted across the full DC market, a realistic average adds roughly 10%.
- Equity: ~$10,000 annualized. DC lags significantly behind SF and Seattle on equity. Federal positions have none. Most defense contractors are publicly traded but issue minimal equity to IC employees. Capital One and some fintech companies do issue RSUs, but the grants are modest compared to Bay Area peers — $40K-$80K over four years for mid-level roles ($10K-$20K annualized) is typical. Pre-IPO startup equity exists but the DC startup ecosystem is smaller and earlier-stage.
Total comp for the median DC data scientist thus runs approximately $161,000, versus $137,120 base. Compare that to a comparable-experience data scientist in Seattle, where base is higher and equity grants at Amazon, Microsoft, or Tableau routinely add $40K-$80K annualized. On total compensation, DC trails the top-tier tech markets by 20-35% for mid-career talent — but the gap narrows significantly once you account for what DC dollars actually buy.
Cost-of-living adjustment
Washington DC carries a cost-of-living index of approximately 152 (US average = 100), based on C2ER and MERIC composite data for 2024. The city ranks among the top five most expensive metros in the US, with housing costs roughly 120-140% above the national average being the dominant driver. Groceries and transportation run 5-10% above average; healthcare roughly at par.
The purchasing-power math: a $137,120 DC salary translates to approximately $90,000 in real purchasing power at the US average cost level. To match that purchasing power in Austin (COL index ~119), you would need to earn roughly $107,000. In a mid-size city like Raleigh or Columbus (COL index ~90-95), the same purchasing power costs only $86,000.
Flipping the comparison: a data scientist earning $120,000 in Columbus, OH has roughly the same real standard of living as a $192,000 earner in DC. The numbers illustrate why remote-first roles that pay DC or national rates but allow you to live somewhere cheaper are so financially attractive for mid-career practitioners.
Where COL math breaks down: the index bundles all goods and services, but data scientists’ largest costs are housing and, for families, childcare. DC area childcare costs average $2,400-$3,000 per month per child — among the highest in the country — which is not fully captured in broad COL composites. High-income households with children face an effective purchasing-power penalty beyond what the 152 index implies.
Negotiation playbook: three levers specific to DC
DC’s employer mix requires a different negotiation strategy than tech-first markets. Here are three levers that actually move the needle here.
1. Anchor to the private-sector comparable, not the federal schedule. If you are interviewing at a defense contractor or consulting firm, the recruiter’s first instinct is to anchor to the GS pay scale. Politely decline that frame. Your counteroffer should reference private-sector data scientist compensation at peer firms — which runs 15-25% above equivalent GS grades — and you should be able to name specific employers (Capital One, the Federal Reserve, Booz Allen’s commercial arm) as reference points. Recruiters at these firms know the data; they are testing whether you do.
2. Use clearance as a hard-dollar premium. If you hold an active TS/SCI or TS/SCI with polygraph, put a specific number on it. The cleared-talent market is transparent enough that you can point to publicly posted cleared data science roles and show that the market prices the clearance at $20K-$35K above equivalent uncleared work. This is not a soft request — it is a documented market rate, and most hiring managers at cleared employers will negotiate within that range rather than risk losing you to another contractor.
3. Negotiate benefits with the same rigor as base. Federal contractors’ non-salary benefits packages vary dramatically, and the delta can exceed $15K-$20K annually in real dollars. Health plan employer contributions, 401(k) match rates (some contractors match up to 6%, others 0-3%), professional development stipends, and tuition reimbursement for certifications (AWS, GCP, relevant domain certs) are all negotiable at the offer stage. Government contractors typically have more flexibility on benefits than on base salary, which is constrained by contract labor categories. Once you have an offer letter, asking “is there flexibility on the 401(k) match or training budget?” is low-risk and often produces a real result.
Caveats on BLS OEWS data
BLS OEWS is the most rigorous public wage source available — mandatory reporting covering hundreds of thousands of establishments — but several limitations are worth understanding before using these numbers to evaluate an offer.
Equity is excluded. BLS captures W-2 wages, which means RSUs, vested options, and profit-sharing distributions are largely absent. For DC data scientists at private employers offering equity, total comp can run $10K-$50K above the reported base figure.
Top-coding. BLS suppresses reported wages above a confidential threshold and replaces them with the threshold value. The P90 figure of $208,600 likely understates what the top 10% of DC data scientists actually earn, especially at senior levels in cleared or finance-heavy roles.
The SOC code is broad. SOC 15-2051 (Data Scientists) covers practitioners whose work ranges from dashboarding and A/B test analysis to training neural networks on petabyte-scale datasets. A junior analyst at an agency and a principal MLOps engineer at a growth-stage AI company are in the same bucket. The percentile spread — $102,440 at P25 to $208,600 at P90 — is a direct consequence of this breadth.
Data lag. May 2024 data reflects wages paid roughly 18-24 months before this page was last updated. The federal locality pay schedule adjusts annually, and private-sector DC salaries for in-demand specialties (ML engineering, LLM deployment) have trended upward through 2025-2026. For the highest-demand roles, add 8-12% to get to current-market figures.
For a complete picture, triangulate the BLS base against OPM locality pay tables for federal roles, posted salary ranges from DC-area employers (DC’s salary transparency requirements mean many postings now include ranges), and Levels.fyi data for employers that participate in verified-comp reporting. That three-source check gets you within 10-15% of any specific offer, which is the precision you need to negotiate effectively.