Data Scientist Salary in Boston — 2026 BLS Data
Salary distribution
Percentile breakdown of Data Scientist base salaries in Boston.
The BLS OEWS May 2024 data for the Boston-Cambridge-Nashua metro area puts the median data scientist base salary at $131,830 — essentially $132,000 rounded. Massachusetts as a whole sits at $132,250, making the state and its dominant metro nearly identical. That national median of $112,590 for the same occupation code (SOC 15-2051) is a useful benchmark: Boston pays data scientists about 17% more than the US average before you factor in what that dollar buys locally. The headline number is useful, but it buries the critical details — the $97,000 gap between P25 and P90, the biotech-versus-finance split, and the fact that BLS doesn’t count a single dollar of equity.
What the $132K median actually hides
The median is a single point on a curve that runs from $80,740 at the 10th percentile to $200,390 at the 90th percentile in the Boston metro. That’s a 2.5x range within one city, one occupation code, and one survey period. SOC 15-2051 collapses junior analysts at insurance carriers, senior ML engineers at Moderna, and quantitative researchers at State Street into the same statistic.
The P25-to-P75 band — $103,000 to $168,000 — is where roughly half of Boston data scientists actually work. A data scientist at a Series B biotech running clinical trial analytics likely sits between $115K and $145K base. A machine learning engineer at a fintech like Toast or Wayfair lands $140K-$175K. An applied research scientist at a big pharma (Biogen, AstraZeneca, Takeda — all with major Boston presences) in a specialized therapeutic area can reach $160K-$185K. The same title, the same BLS code, three materially different markets.
What the median also hides is level. Data scientist roles at most Boston employers span at least three rungs — IC1/DS1, IC2/DS2, IC3/senior DS — with base salaries roughly $95K-$115K, $120K-$145K, and $150K-$175K respectively. When a recruiter quotes you “our data scientist comp range,” they’re often quoting a span that covers all three. Knowing where on that spectrum a specific role actually sits — before you receive a number — is the single most valuable piece of information you can gather during the process.
Boston versus other major data science hubs
Boston is not San Francisco or New York, and the data reflects that clearly. SF median base for data scientists runs around $165K-$185K in comparable 2024 survey data, with a total comp distribution heavily tilted by Big Tech equity. New York pulls $145K-$165K at the median, driven partly by quantitative finance roles that pay $180K-$250K base but exist in a narrow slice of the market.
Boston’s $132K median puts it solidly in the third tier — comparable to Seattle and Chicago on base salary, though each city has a different mix of employers. Where Boston diverges from Seattle is in industry composition. Seattle is overwhelmingly tech (Amazon, Microsoft, Expedia, Zillow). Boston’s data science market is more diversified: pharma and biotech (Biogen, Moderna, Vertex, Sanofi, Takeda, Pfizer’s Cambridge campus), financial services (Fidelity, State Street, Liberty Mutual, MassMutual), healthtech (athenahealth, Veeva, Definitive Healthcare), and a smaller but growing tech cluster around Cambridge and the Seaport.
That diversification creates an interesting pay structure. Boston biotech data scientists often earn less in base than their finance-sector peers but receive milestone bonuses and sometimes stock options on pre-IPO companies that can be worth multiples of salary if the drug reaches approval. A financial-services DS at State Street or Fidelity earns more predictable total comp but with limited equity upside. Neither path dominates; they’re simply different risk/reward profiles.
Austin ($115K-$125K median base) and Denver ($110K-$120K) trail Boston on raw salary by 10-20%, but their cost-of-living indices run well below Boston’s 162.0. The purchasing-power math is covered below.
What drives the spread: company tier, level, and specialty
Industry and employer tier is the largest lever. Big Pharma and major financials pay at or slightly above market median with strong benefits and predictable annual raises. Mid-stage biotech (Series C through pre-IPO) pays aggressively on base to compete without public-company equity — $140K-$165K for a mid-level role is common. Early-stage biotech tends to underpay on base and lean on option narratives. Tech companies (Wayfair, HubSpot, Rapid7, iRobot before the Amazon acquisition) generally land at market with better title-to-pay efficiency than pharma.
Seniority drives roughly a 60-70% spread from DS1 to Principal/Staff. An entry-level data scientist in Boston coming out of an MS program can realistically expect $90K-$108K (P25 range). A senior data scientist with 5-7 years and a strong modeling portfolio lands $148K-$168K (P75 range). A staff or principal DS with domain expertise in a scarce area — clinical genomics, NLP for drug discovery, credit risk modeling — can reach the $185K-$200K band.
Specialty premium is real and growing. As of 2024-2026, roles that blend data science with ML engineering, MLOps, or LLM fine-tuning command 15-25% premiums over generalist DS roles at the same level. Boston’s biopharma cluster is creating demand for computational biology and AI-for-drug-discovery skills that the market hasn’t fully priced yet — roles combining Python/ML with biology domain knowledge are often 10-15% above equivalent pure-software DS positions. Conversely, traditional BI-adjacent “data scientist” roles that are primarily SQL and dashboard work often pay at or below the $103K P25 threshold even when the title doesn’t signal that.
Education premium has compressed but hasn’t vanished. An MS from a Boston-area program (MIT, Harvard, Boston University, Northeastern) adds a signaling premium for junior roles that typically disappears by year three as the market weights portfolio and impact over credential. A PhD carries a genuine salary premium at pharma/biotech research roles — $10K-$20K above matched non-PhD peers — because experimental design and publication experience are actually used. That premium largely doesn’t exist in tech-sector DS roles.
Total compensation breakdown
BLS captures base salary only. For a mid-level data scientist in Boston (think 3-5 years of experience, targeting the P50-P75 range), here is a realistic total compensation picture:
- Base salary: $132,000. This is the BLS-tracked number, what appears on offer letters, and what determines your 401(k) match, insurance premiums, and mortgage pre-qualification. In financial services and most corporate employers, base is the largest and most stable component.
- Annual cash bonus: ~$18,000. Pharma and financial services typically pay 10-15% of base as a cash bonus. At Biogen, Fidelity, and Liberty Mutual, these are formula-driven and paid reliably when the company hits its plan. Startups and growth-stage tech companies sometimes replace the bonus structure with a higher base or front-load equity instead.
- Equity (annualized): ~$25,000. This is the most variable element. At a public tech company (Wayfair, HubSpot), annualized equity at mid-level is roughly $20K-$40K in RSUs. At pre-IPO biotech, options may be worth significantly more or significantly less depending on clinical outcomes — a successful Phase III trial can make a $0.15 option grant worth $15-$30 per share; a failed trial makes it worth zero. At public financial services firms (State Street, Fidelity), equity grants are modest or nonexistent below the managing director level. The $25K is an average across sector mixes; your specific situation may be zero equity (insurance, traditional finance) or $60K+ (late-stage public tech).
Total compensation of $175,000 is therefore a reasonable central estimate for a mid-level Boston data scientist — consistent with Blind’s reported $175,375 median TC for the market. Senior data scientists targeting the P75-P90 band are looking at $200K-$250K total comp when equity is included, with the top of that range typically requiring either FAANG-adjacent tech roles or a successful biotech equity event.
Cost-of-living adjusted picture
Boston’s cost-of-living index of 162.0 means it costs 62% more to live here than the US average. On purchasing power grounds, a $132,000 Boston base is equivalent to about $81,500 at the national average price level — or roughly $97,000 in Austin (COL ~136), $88,000 in Denver (COL ~150), and $74,000 in Raleigh (COL ~110).
That math is harsh. It’s why Boston data scientists making six figures still feel financially constrained in ways that don’t show up in the headline number. The driver is housing: the Boston metro median home price ran approximately $620,000 in 2024 according to the Greater Boston Housing Report Card, and a one-bedroom apartment in Cambridge or the South End typically runs $2,800-$3,600 per month. A data scientist earning $132K gross takes home roughly $8,500-$9,000/month after federal and Massachusetts income tax (5.0% flat rate). Rent at $3,200 consumes 35-38% of take-home — above the 30% threshold that most financial planners flag.
The counterpoint: Boston is one of the few cities where the industry ecosystem justifies the premium. The concentration of pharma, biotech, hospital systems (MGH, Brigham, Dana-Farber), and world-class universities (MIT, Harvard, Tufts, BU, Northeastern) creates a thick market for data science talent. Career optionality — the ability to move from clinical DS at Vertex to applied research at the Broad Institute to an ML role at a healthtech startup without leaving the metro — is a real, non-monetary form of compensation. Cities with cheaper rent often have thinner markets where a layoff requires a geography change.
Compared to San Francisco (COL ~178.6): a $132K Boston salary has noticeably more purchasing power than a $165K SF salary. Compared to the national average: it buys you 19% less than it appears on paper.
Three-lever negotiation playbook for Boston data scientists
Lever 1: Anchor to BLS P75 on base, not P50. Most hiring managers in Boston know the market. Walking in with $132K as your anchor signals you’ve done only basic research and gives them a convenient midpoint to work toward. If you have 3+ years of experience and a concrete portfolio, $148K-$155K as your opening base for a senior DS role is defensible and not uncommon. Reference the BLS P75 of $168K explicitly if you need to justify the number — it’s government data, not a self-reported survey, and it lands differently than “Glassdoor says.” The ask should feel ambitious but grounded.
Lever 2: Negotiate the bonus formula and vesting schedule explicitly. In Boston, especially in pharma and financial services, the bonus and equity structure matter as much as base. Before accepting an offer, ask: Is the bonus discretionary or formula-driven? What was the actual payout percentage last year? When does equity vest, and does accelerated vesting apply on acquisition? At a pre-IPO biotech, these questions can differentiate an offer that’s worth $180K from one that’s worth $140K — at the same stated base salary. Companies that resist answering are telling you something important.
Lever 3: Use competing offers from adjacent sectors. Boston’s multi-sector market is unusually useful here. A DS offer from a fintech like Toast or Klaviyo creates legitimate leverage for a pharma role, and vice versa. Recruiters at Biogen or Moderna know that Fidelity and MassMutual pay $20K more on base for comparable roles — if you have a financial-services offer in hand, that’s a concrete number that a pharma hiring manager can bring to HR. Similarly, a late-stage biotech equity package with real upside — say, a company 12-18 months from potential approval — can be used as leverage against a public tech company that pays higher base but no meaningful equity. Cross-sector competing offers are more persuasive than same-sector ones because they force a company to justify its overall value proposition, not just argue about whether their band is $5K higher than a competitor’s.
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 in the survey reference period. Bonuses, equity, profit-sharing, and the value of benefits are all excluded. For tech roles, BLS understates total comp by 20-40%. For pre-IPO biotech roles, the understatement is either larger or negligible depending on whether you believe the equity is worth anything.
May 2024 data has roughly a 12-18 month lag by mid-2026. The OEWS survey is conducted in May and released roughly six months later. It reflects wages paid in the spring of 2024, not what the market is doing today. Given that the overall US data science labor market softened in 2023-2024 after the hiring freeze that followed the 2022 rate environment, current 2026 wages are likely 5-10% above the BLS numbers in active hiring roles as the market has recovered.
SOC 15-2051 is a broad category. It includes machine learning engineers, data scientists, and quantitative analysts at firms that chose to classify roles under that code. It does not include data engineers (15-1243) or statisticians (15-2041). If the role you’re evaluating is primarily pipeline/infrastructure work, the 15-1243 benchmarks are more relevant. If it’s purely statistical modeling for research, 15-2041 may apply.
Metro area data has smaller sample sizes. State-level BLS numbers are more statistically stable than MSA-level estimates. The Boston metro figures are directionally reliable but may have wider confidence intervals than the Massachusetts state numbers. For P25-P50, the state and metro are essentially identical; at P90, there can be modest variation quarter to quarter.
For supplement benchmarking, triangulate BLS base with Levels.fyi total comp data for Greater Boston (which shows a $159,500 median total comp across all levels in 2026) and with Massachusetts salary transparency laws — the state does not yet mandate salary range disclosure on job postings, but many companies that post nationally include ranges in compliance with other states’ laws. Those posted ranges, combined with BLS percentiles and Levels total comp, get you to within 10-15% of what any specific offer should look like before you ever enter a negotiation room.