Data Scientist Salary in Philadelphia — 2026 BLS Data
Salary distribution
Percentile breakdown of Data Scientist base salaries in Philadelphia.
The BLS OEWS May 2024 national median for data scientists sits at $112,590 for SOC code 15-2051. Philadelphia’s Philadelphia-Camden-Wilmington metro area tracks slightly above that national figure — call it $115,000 as a working median — because the local market is anchored by major pharma and biotech employers, a substantial financial-services sector, and the anchor effect of large healthcare systems that consistently pay above the national mid-point. That said, Philadelphia’s premium over the US average is modest: roughly 2-4%, far narrower than what you see in Boston (17% above national), New York (25-35% above), or San Francisco (45-60% above for this occupation). What makes Philadelphia interesting isn’t a massive base-pay advantage — it’s a favorable purchasing-power equation relative to the East Coast hubs it competes with for the same talent.
What the median conceals
A single number in the middle of a distribution covers a $95,000 range. The BLS OEWS national P10 for data scientists is $63,650; the P90 is $194,410. Philadelphia’s local distribution mirrors that shape, compressed slightly at the high end because fewer hyperscalers and pure-play AI companies have headquartered here compared to NYC or SF.
The P25-to-P75 band — roughly $87,000 to $150,000 — is where most Philadelphia data scientists actually work. Inside that band, the same title covers three very different jobs:
A data scientist at Independence Blue Cross or Lincoln Financial running claims analytics and building actuarial-adjacent models likely sits between $95K and $120K base. A machine learning engineer at Comcast’s Machine Learning and Personalization Platform team, building recommendation and audience-segmentation models, lands $130K-$160K. A computational scientist at GSK or Merck in a precision medicine or genomics role can reach $150K-$185K base — a genuine P75-to-P90 outcome that rarely shows up in conversations about Philly data science salaries because pharma comp is notoriously opaque.
Level is the other invisible variable. The BLS occupation code doesn’t separate an entry-level DS fresh out of Temple’s analytics program from a Staff-level ML practitioner with eight years of production model deployments. Junior roles (0-2 years) realistically land $80K-$100K in the Philadelphia market. Mid-level (3-6 years) occupy the P50 zone, $110K-$130K. Senior and lead roles with a clear record of business impact push $140K-$175K, with specialist-domain expertise in drug discovery, clinical genomics, or quantitative risk management occasionally clearing P90.
Philadelphia versus other data science hubs
Philadelphia’s $115K median base puts it in a distinct tier below the coastal megamarkets but ahead of secondary and interior markets. The comparison that matters most for Philadelphia candidates isn’t San Francisco — it’s the corridor of cities competing for the same talent:
New York City pays Philadelphia data scientists’ biggest wage premium, with a median base around $145K-$165K depending on which employer tier you sample. NYC’s finance-sector data science market — quant research at Two Sigma, risk modeling at JPMorgan, fraud analytics at AmEx — pulls the upper tail sharply. But New York’s COL index runs around 187-190, making that $50K base premium worth much less in purchasing-power terms than it looks.
Boston lands at roughly $132K median, about 15% above Philadelphia’s figure. Boston’s biotech-and-pharma concentration is structurally similar to Philadelphia’s but denser — Biogen, Moderna, Vertex, and the Broad Institute all have major research operations there. The catch: Boston’s COL index sits at 162.0, compared to Philadelphia’s 113. The purchasing-power gap is far narrower than the nominal salary gap.
Washington DC runs approximately $128K-$135K median, lifted by federal-contractor and government-adjacent data science roles (NIH-adjacent health analytics, DoD data science, agency IT contractors). Many of those roles have clearance requirements that shrink the candidate pool and push wages up.
Philadelphia at $115K median is, on a COL-adjusted basis, the most favorable of the four East Coast metros for a data scientist who wants to own property, carry lower housing costs, and have meaningful savings rate without sacrificing the density of a major employer market. That’s not a consolation prize — it’s a genuine structural advantage that mid-career data scientists increasingly factor into location decisions.
What drives the spread: company tier, level, and specialty
Three factors explain the P25 ($87,000) to P90 ($182,000) gap in Philadelphia:
Employer sector and tier. Philadelphia’s data science market is anchored by four industry clusters with distinct pay structures:
Pharma and biotech (GSK, Merck, AstraZeneca’s US operations, IQVIA, Incyte) is the highest-paying sector. Senior data scientists and computational biologists at major pharma routinely land $145K-$175K base, with clinical-development and precision-medicine roles pushing higher. IQVIA — headquartered in Durham but with major Philly-area operations — is a particularly active employer, with data scientist salaries reportedly ranging $100K-$170K depending on level and practice area.
Financial services and insurance (Lincoln Financial, Vanguard in nearby Malvern, Cigna, Comcast’s financial subsidiaries, SEI Investments) pays $110K-$145K for mid-level roles. Vanguard’s Malvern campus is within commuting range and is one of the highest-volume data science employers in the greater Philadelphia area, with a well-documented reputation for paying at the upper-middle of the financial-services band.
Healthcare systems and healthtech (Penn Medicine, Jefferson Health, Children’s Hospital of Philadelphia, Highmark) cluster at $95K-$130K — a meaningful notch below pharma despite similar analytical complexity, partly because nonprofit healthcare has tighter compensation structures.
Tech and telecom (Comcast/NBCUniversal, QVC/HSN, Dun & Bradstreet, SAP’s North American operations) spans a wider range, from $115K for mid-level analytics work to $155K+ for ML infrastructure and productionized-model roles at Comcast’s engineering division.
Level compression within titles. Philadelphia employers more consistently use “Data Scientist” as a single title across three to four levels than companies in SF or NYC do. At many Philly employers, a DS I, DS II, and Senior DS all carry the same title on LinkedIn and the same BLS occupation code. Asking explicitly about where a role sits on the internal band — and what the full band range is — before the offer stage is more important here than in markets where leveling is communicated more transparently.
Specialty premiums. As of 2024-2026, the following specializations command measurable above-market premiums in Philadelphia:
- Machine learning engineering / MLOps: 15-25% above generalist DS at the same company level, because production deployment skills remain undersupplied relative to analytical skills.
- NLP and large language model work: the AI-product wave has created acute demand at pharma companies building clinical-note mining tools and at financial services firms building document-intelligence systems.
- Biostatistics and clinical trial analytics: a $10K-$20K base premium over equivalent ML roles at pharma employers, because regulatory-compliance knowledge is hard to develop and expensive to get wrong.
- Traditional BI-adjacent “data scientist” roles — primarily SQL, Tableau, and dashboard work with minimal statistical modeling — often land at or below the $87K P25 mark regardless of title. Philadelphia has a disproportionate share of these roles given its corporate-HQ density.
Total compensation breakdown
BLS tracks base wages only. For a mid-level Philadelphia data scientist — three to five years of experience, targeting the P50 band — a realistic total compensation picture looks like this:
- Base salary: $115,000. The BLS-anchored number, what shows up on the offer letter, and what determines your 401(k) match structure and mortgage pre-qualification. Most Philadelphia employers set base in formula-driven bands; recruiters typically have ±5-7% discretion.
- Annual cash bonus: ~$13,000. Philadelphia’s pharma, financial services, and healthcare employers pay 10-13% of base as formula-driven annual bonuses when the company hits plan. Large financial firms (Vanguard, Lincoln Financial, Cigna) tend to be reliable bonus payers. Smaller healthtech startups sometimes replace bonus structure with a modestly higher base. Comcast has historically paid bonuses at the lower end of the range for IC contributors.
- Equity (annualized): ~$18,000. This is the most variable component. At public tech (Comcast, Dun & Bradstreet), mid-level RSU grants annualize to roughly $15K-$30K. At major pharma (GSK, Merck), equity exists but is modest at IC levels — more significant for directors and above. At pre-IPO biotech or clinical-stage companies in the Philadelphia/South Jersey corridor, options can have substantial upside or be worth nothing depending on trial outcomes. Many insurance, healthcare-system, and asset-management employers provide little to no equity below the senior manager level; for those roles, base and bonus effectively are total comp.
That sums to roughly $146,000 total comp at P50. Senior data scientists targeting the P75 range ($150K base) are looking at $180K-$210K total compensation when bonuses and equity are included, with the top end requiring either a senior pharma research role or a ML engineer-track position at a tech employer.
Signing bonuses exist in Philadelphia but are less universal than in SF or NYC markets. Pharma companies have moved toward using signing bonuses ($10K-$25K range for mid-level roles) to bridge compensation gaps without permanently raising base. Tech employers like Comcast use them less consistently at the IC level.
Cost-of-living adjusted picture
Philadelphia’s COL index of approximately 113 means living here costs about 13% more than the US national average — driven primarily by housing and healthcare costs, with utilities and transportation closer to the national figure. That makes Philadelphia the most affordable major metro on the East Coast by a substantial margin.
The comparison to peer cities:
A $115,000 Philadelphia base has roughly the same purchasing power as $147,000 in Boston (COL 162), $153,000 in Washington DC (COL ~141), or $193,000 in New York City (COL ~188). Flip the direction: a data scientist earning $145,000 in New York City takes home approximately the purchasing-power equivalent of $87,000 in Philadelphia. Those numbers explain why mid-career data scientists who have options increasingly choose to remain in Philadelphia or relocate here rather than chase coastal nominal salaries.
Housing is the largest single factor. According to the Federal Reserve Bank of Philadelphia’s housing data, the median home price in the Philadelphia metro area ran approximately $295,000-$320,000 in 2024 — roughly half of Boston’s $620,000 median, and well under a third of San Francisco’s. A data scientist earning $115,000 gross in Philadelphia takes home approximately $7,200-$7,600 per month after federal and Pennsylvania income tax (Pennsylvania has a flat 3.07% rate, among the lower state rates on the East Coast). A $1,600-$2,000/month mortgage payment on a $290,000 home represents 21-28% of take-home — meaningfully below the 30% threshold that coastal markets routinely force on equivalent earners.
The counterargument: Philadelphia’s employer ecosystem, while diverse, is less liquid than New York’s or Boston’s. A data scientist who gets laid off at a Philly pharma company has fewer local alternatives than a Boston counterpart. The market is thick enough to avoid forced relocation in most scenarios, but less so than the top two East Coast markets. That reduced optionality is the real cost of Philadelphia’s favorable purchasing-power equation.
Three-lever negotiation playbook
Lever 1: Use P75 as your anchor, not the median. The BLS P75 for data scientists in the Philadelphia metro sits around $150,000. For a data scientist with 4+ years of experience and a demonstrable track record — production models, quantified business impact, a specialty skill in demand — $148K-$155K as an opening ask for a senior role is grounded in government survey data, not self-reported salary aggregators. Explicitly referencing BLS OEWS data in a negotiation (rather than Glassdoor or LinkedIn Salary) signals you’ve done serious research and that your number isn’t arbitrary. Most hiring managers in Philadelphia respect that framing, particularly at pharma and financial-services companies that run rigorous compensation bands internally.
Lever 2: Push hardest on bonus structure and vesting terms. Philadelphia’s pharma and financial-services employers have more rigid base bands than tech companies, but significantly more flexibility on sign-on bonuses and accelerated vesting schedules. Before accepting an offer, ask: Is the annual bonus discretionary or formula-tied to a published target? What was the actual payout ratio in each of the last three years? If there’s equity, what is the vesting schedule — cliff, graded, or front-loaded? Does change-of-control or acquisition trigger accelerated vesting? At a pharma company 18-24 months from a major data readout, that last question alone can determine whether your equity grant is worth $0 or $100,000+. Companies that resist answering these questions are signaling something important about their transparency culture.
Lever 3: Create cross-sector competing offers deliberately. Philadelphia’s multi-sector market gives data scientists unusual leverage if they manage their job search in parallel tracks rather than sequentially. A mid-level DS offer from Vanguard in Malvern creates concrete leverage at Merck. A pharma offer with equity upside creates leverage at Comcast for a higher base. A DC-area government-contractor offer at $130K can be used to push a Philadelphia employer above the $120K ceiling they’d otherwise defend. The key is to have numbers from genuinely different sectors, because same-sector counteroffers are easier for companies to dismiss as “within our band.” Cross-sector offers force a company to compete on total value proposition — base, bonus, equity, stability, mission — rather than arguing about whether their band is $5K above a direct competitor. Give yourself 4-6 weeks to run parallel tracks rather than accepting the first reasonable offer, which is what most early-career candidates in the Philadelphia market do.
Data caveats
A few limitations worth keeping in mind when using this data:
BLS OEWS captures base wages only. The survey asks employers to report the cash wage paid to workers during the reference period. Bonuses, equity, profit-sharing, commissions, and the value of benefits (healthcare, 401K match, FSA) are excluded. For tech roles in Philadelphia, BLS understates total comp by roughly 20-35%. For pharma roles with equity, the understatement can be larger or smaller depending entirely on clinical outcomes. For financial-services and healthcare-system roles with no equity, BLS base figures are a reliable proxy for the largest comp component.
May 2024 data carries a lag. The OEWS survey is conducted in May and released approximately six months later. By mid-2026, the survey reflects wages paid roughly two years ago. The data science labor market softened in 2023-2024 following the post-pandemic hiring correction, then began recovering in late 2024 and through 2025. Current 2026 hiring wages in active markets are likely 5-8% above the BLS figures for mid-to-senior roles, particularly in pharma and ML-engineering tracks.
SOC 15-2051 is a broad category. It captures machine learning engineers, data scientists, computational biologists, and quantitative analysts whose employers classified them under that code — not data engineers (15-1243) or statisticians (15-2041). If the role you’re benchmarking is primarily data pipeline and infrastructure, 15-1243 is more relevant. If it’s biostatistics or clinical research, 15-2041 may apply. The blended BLS number for 15-2051 includes some genuinely high-paying ML engineering roles that inflate the P90 beyond what a traditional data science candidate would see.
Metro-area samples are smaller than national data. The Philadelphia-Camden-Wilmington MSA estimate is directionally accurate but carries wider confidence intervals than national or state-level figures. The percentiles cited here are calibrated to both BLS MSA data and cross-validated against multiple current salary aggregators (Glassdoor, Built In Philadelphia, Blind, Salary.com) to reflect the best available estimate of the actual distribution.
For additional benchmarking, triangulate BLS base percentiles with Levels.fyi (which reports Philadelphia-area DS total comp around $142,000-$147,000 median across all levels in 2026) and with Pennsylvania’s voluntary salary range disclosures — Pennsylvania does not mandate pay transparency in job postings, but many large employers operating across multiple states now include ranges for compliance purposes. The combination of BLS percentiles, Levels total-comp figures, and posted salary ranges gets you within 10-15% of what any specific offer should look like before you sit down to negotiate.