AutomateEdge · Data Scientist · Salary

Data Scientist Salary Bands 2026: US + Regional

The salary ranges below are US market aggregates for Data Scientist titles in 2026 Q1. An honest 2026 framing: pure analytics-DS roles have compressed modestly in pay and prestige relative to ML Engineer and AI Engineer specialisations, as ML tooling commoditised and the modelling share of DS work decreased. That said, senior DS with causal inference, experimentation, and business-communication depth remain highly compensated — particularly at product-first companies where A/B testing, growth, and decision-science are strategic functions. Regional multipliers are rough guides; specific-company packages vary widely. Velocity data comes from the AutomateEdge jobs pipeline.

Data last refreshed — 2026-04-24

US compensation by seniority

All figures in USD. Base salary is cash annual; total comp includes base + equity (vested value) + bonus. Ranges show 25th-75th percentile — the middle half of actual offers seen over the trailing 180 days.

LevelBase salaryTotal compNotes
Entry (L2/L3, 0-2 yrs) $105,000 – $145,000 $125,000 – $200,000 Most entry DS roles require a BS in a quantitative field (statistics, math, CS, economics) or a strong portfolio equivalent. SQL + Python are hard requirements at screening; statistics is probed in the interview loop. Entry DS at brand-name product companies now competes directly with entry data analysts for base, with DS roles winning on equity.
Mid (L4, 2-5 yrs) $135,000 – $185,000 $175,000 – $300,000 The largest band by population. Mid-level DS who own experimentation programs or causal studies are at the high end. Those doing primarily dashboarding and reporting are at the low end. The split is real and is worth being aware of — scope shapes comp at this level.
Senior (L5, 5-8 yrs) $175,000 – $235,000 $245,000 – $450,000 Compensation at L5 is dominated by equity. Senior DS at growth-stage companies with high experiment velocity and causal ownership can clear $400k TC. Senior DS at BI-heavy roles at traditional enterprises may sit at $200-250k TC. The spread reflects scope, not just title.
Staff (L6, 8-12 yrs) $215,000 – $300,000 $360,000 – $680,000 Staff DS is rare and increasingly takes on a Decision Science or Applied Research flavour — framing multi-quarter experiments, owning causal inference methodology for a product area. At hyperscalers, Staff DS pay approaches ML Engineer pay at this level. At smaller companies, the title may not exist.
Principal (L7+, 10+ yrs) $270,000 – $380,000 $530,000 – $1,100,000 Rare. Frontier labs and FAANG only at this level. Often cross-functional: setting experimentation policy, directing a causal inference center of excellence, or leading a research team. Comp competes with Director and VP-level engineering at mid-market companies. The $1M+ band is achievable but narrow.

Regional multipliers (apply to US base)

These are directional multipliers, not precise mappings. India and Southeast Asia use local-currency bands directly — see the notes column.

RegionMultiplierNotes
SF Bay Area 1.15x Reference market. DS pay in the Bay Area is driven by competition from ML Engineer and AI Engineer roles; companies pay up to retain analytical talent that could easily pivot to higher-paying specialisations.
NYC / Seattle / Boston 1.08x Near-peer to SF for base. NYC fintech and hedge-fund DS roles can clear SF comp at mid-senior levels. Seattle's Amazon presence creates a strong anchor for data roles.
Other major US metros (Austin, Chicago, Denver, Raleigh) 0.93x Modest discount vs. SF. Remote policy compression has closed the gap to roughly 7-8% below SF in 2026 for comparable roles.
Fully remote US 0.90x Remote-band convergence has stabilised. DS roles typically have slightly more remote flexibility than ML Engineering roles, making this a meaningful option. Expect a 10% discount vs. co-located SF equivalent.
Toronto / Montreal (CAD) 0.63x CAD comp runs materially below US at every level. Toronto's financial-services DS market is strong and partially mitigates the discount for mid-senior roles.
Western Europe (EUR) 0.52x Strong discount across the board. London DS roles at hedge funds and fintech companies are exceptions — London can approach US senior-level comp for quantitative DS roles. Amsterdam tech companies pay in the middle of the European range.
India (INR) — Separate market. Entry: ₹8-16 LPA. Mid: ₹18-40 LPA. Senior at product companies + GCCs: ₹45-100 LPA. Top-of-market (frontier lab India offices, staff-level at Tier-1 product companies): ₹120-180 LPA.
Southeast Asia (SGD / MYR) — Singapore: entry S$55-80k, mid S$90-140k, senior S$150-260k. Strong demand in fintech and ride-hailing companies. Other SEA markets (Malaysia, Vietnam, Indonesia) run 40-60% below Singapore.

90-day hiring velocity

Open-role volume — The AutomateEdge pipeline tracked approximately 1,800 US-based roles with 'Data Scientist' in the title over the trailing 90 days — up 9% year-over-year. Pure DS roles grew more slowly than AI Engineer and ML Engineer roles, consistent with the title compression trend where companies rebrand analyst-heavy DS roles upward to attract ML-capable candidates.

Time to fill — Median time-to-fill for entry-level DS roles: 45 days. Senior DS roles: 72 days — longer than ML/AI engineer equivalents, reflecting higher specificity in domain and statistical-methodology requirements at senior levels.

Top hiring companies (trailing 90 days)

Skills moving the market most

Negotiation playbook

Data sources

Frequently asked questions

Do Data Scientists get paid less than ML Engineers in 2026?

Slightly, on average. Entry and mid levels overlap almost completely, but at senior and above, ML engineers who own production model infrastructure and AI engineers building LLM-powered products command a 15-25% premium. The reason is infrastructure ownership: an ML engineer who can optimise inference or scale training is a harder replacement than an analytically excellent DS. The gap is not a value judgement — it reflects market scarcity.

Has the DS market compressed since 2022?

Yes, measurably. 'Data scientist' job postings peaked in 2021-2022 alongside the analytics boom, then compressed in 2023-2024 as companies tightened headcount and as the LLM wave made 'AI engineer' the new prestige title. The market recovered in 2025-2026, but the role has differentiated: high-paying DS roles increasingly require causal inference and experimentation expertise, not just Python + ML familiarity. Pure-analytics DS titles are on slower trajectories.

How much does remote work affect DS compensation?

About 10% discount vs. co-located equivalent in 2026. The gap has narrowed from 20%+ in 2022. DS roles tend to have more remote flexibility than ML/AI engineering roles because the work is less infrastructure-dependent. For senior-level remote DS with deep experimentation skills, the discount may be smaller — those skills are scarce enough that companies relax location requirements.

Is equity meaningful in DS compensation?

At public hyperscalers (Google, Meta, Amazon, Netflix), equity is the dominant compensation variable at L5+ — it can be 40-60% of TC. At private companies, equity has real liquidation risk: a $500k paper equity grant from a Series C startup should be mentally haircut 50-70% when comparing to public-company equity. Get the fundamentals (preferred vs. common, strike price, waterfall) before valuing private equity.

What skills most change your negotiation leverage as a DS?

In 2026, two: (1) demonstrated causal inference work — not just 'I know what DiD is' but a portfolio project that ran a real quasi-experiment with robustness checks; and (2) experimentation platform ownership — evidence that you have owned the A/B testing workflow, not just consumed experiment results. Both signal a specialisation that commands a scarcity premium at mid-to-senior levels.

How do DS salaries differ across industries?

Tech (product companies): highest, following the bands above. Finance / fintech / hedge funds: competitive at mid-senior, occasionally above tech at very senior quant-DS roles. Healthcare / pharma: 15-25% below tech at comparable levels but with stronger job stability. Retail / CPG: 20-30% below tech. Government / non-profit: 35-50% below, partially offset by defined-benefit pensions and work-life balance. Industry choice at year 3-4 has large long-term comp implications.

Can a Data Scientist reach $500k TC without a management role?

Yes, on the IC track at Staff+ levels at hyperscalers or high-growth AI companies. It requires being excellent at both the technical work (deep statistical methodology, causal reasoning) and the organisational work (setting the experimentation culture, publishing externally, being the person VPs call for hard analytical questions). The population is small; the path is real.

Does a PhD significantly increase DS compensation?

At entry, a PhD typically commands a 15-20% base premium and a level-bump (starting at L4 rather than L3). By mid-senior levels, the PhD premium converges to near zero as demonstrated project work and scope ownership dominate comp. PhDs are still over-represented at frontier labs and research-adjacent DS roles, where methodology depth and publication record matter. For product DS roles, the premium does not persist past 3-4 years of strong performance.

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