Compare · Software Engineer vs Data Scientist
SWE vs Data Scientist: Real Comp, Day-to-Day Tasks, Career Switch
Levels.fyi-grade real total-comp by level (not Glassdoor self-report bias) + O*NET-grounded daily task percentage breakdown + asymmetric switching difficulty (SWE→DS easier than DS→SWE) + industry premium analysis (DS in non-tech significantly underpays vs SWE)
Side-by-side comparison table — TODO: populate Software Engineer vs Data Scientist on salary / authority / school / outlook.
SWE vs DS in 90 Seconds
The roles share a Python prompt and not much else. Software engineers build software systems — they ship code that runs in production, gets tested, scales to traffic, and earns the company's revenue. Data scientists extract insight from data — they ship analyses that change a business decision, models that score new data, or experiments that resolve an uncertain question. Both pay well; the bottom line is which work you want to do every day.
| Dimension | Software Engineer | Data Scientist |
|---|---|---|
| Primary deliverable | Production code | Insight or model that changes a decision |
| BLS code | 15-1252 (Software Developers) | 15-2051 (Data Scientists) |
| National median (BLS May 2024) | $132,270 | $108,020 |
| Top 10% (P90) | $208,620 | $184,090 |
| Tech-industry top | $350K–$700K+ at FAANG senior | $300K–$500K+ at FAANG senior |
| Typical entry path | CS degree or bootcamp + coding portfolio | Stats/CS/quantitative master's preferred |
| Day-to-day (rough %) | 60–70% writing/reviewing code, 20% design, 10% meetings | 30–40% data wrangling, 20–30% analysis, 20% communication, 10% modeling |
| Career ceiling at non-tech | $160K–$220K (sr. eng / staff) | $140K–$180K (lead DS) |
| Career ceiling at tech | L7+ Distinguished Engineer $700K–$1M+ | Principal DS $500K–$800K (rare) |
| Job market depth (2026) | Mature; strong; AI/ML eng is hottest sub-niche | Maturing; recent contraction; AI/ML side is hot |
Real Compensation Data (Not Glassdoor Self-Report)
Glassdoor and Indeed rely on self-reported salary entries, which systematically inflate by 8–18% vs BLS Employer Costs and skew toward the upper tail (people with high comp report more). Levels.fyi captures more reliable data via offer letter screenshots, but is heavily weighted to FAANG/tech. The honest picture combines both — BLS for the broad floor, Levels.fyi for the tech ceiling.
Software Engineer total comp by level (Levels.fyi-like data, U.S.)
| Level | Title (typical) | FAANG total comp | Mid-tier tech | Non-tech (Fortune 500) |
|---|---|---|---|---|
| L3 / IC1 | SWE I / Junior | $180K–$220K | $110K–$150K | $80K–$110K |
| L4 / IC2 | SWE II | $240K–$320K | $140K–$200K | $100K–$140K |
| L5 / IC3 | Senior SWE | $340K–$500K | $200K–$280K | $140K–$180K |
| L6 / Staff | Staff Engineer | $500K–$750K | $280K–$400K | $180K–$240K |
| L7 / Senior Staff | Senior Staff / Principal | $750K–$1.2M | $400K–$600K | $220K–$300K (rare) |
| L8 / Distinguished | Distinguished Engineer | $1M–$2M+ | $600K–$1M (rare) | — |
Data Scientist total comp by level (U.S.)
| Level | Title (typical) | FAANG total comp | Mid-tier tech | Non-tech |
|---|---|---|---|---|
| L3 | DS I | $160K–$200K | $95K–$135K | $75K–$100K |
| L4 | DS II | $220K–$300K | $130K–$180K | $95K–$130K |
| L5 | Senior DS | $320K–$450K | $180K–$250K | $130K–$170K |
| L6 | Staff DS | $450K–$650K | $250K–$350K | $170K–$220K |
| L7 | Principal DS | $650K–$1M (rare) | $350K–$500K (rare) | — |
Total comp = base + RSU/equity at vest + sign-on amortized + target bonus. Ranges blend Levels.fyi public submissions, Blind, and recruiter-published comp bands. Treat as directional within ±15%.
The pay-gap pattern: SWE earns ~10–25% more than DS at every comparable level, in every industry, in every market. The gap reflects (1) deeper pipeline of trained SWE candidates supplying the labor market, (2) higher revenue attribution per SWE in most company P&Ls, and (3) DS roles being more often classified as cost-center vs SWE as profit-center. The recent ML/AI engineering specialty has erased the gap for hybrid roles — but pure DS continues to underearn pure SWE.
What You'll Actually Do (Task Breakdown)
The most useful comparison isn't compensation — it's whether you'll enjoy the daily work. O*NET task statements (15-1252 vs 15-2051) plus practitioner survey data give the cleanest picture.
Software Engineer — typical daily mix
- Writing production code (35%) — implementing features, fixing bugs, refactoring
- Code review (15%) — reviewing peers' PRs, responding to comments on your own
- System design / architecture (12%) — designing new components, capacity planning, RFCs
- Testing & debugging (10%) — writing tests, investigating production issues
- Meetings (10%) — standups, design reviews, 1:1s
- Documentation (5%) — runbooks, design docs, API docs
- Tech-debt / infrastructure (8%) — upgrades, refactors, build-system maintenance
- Other (5%) — interviewing, mentoring, on-call
Data Scientist — typical daily mix
- Data wrangling / cleaning (30%) — joining sources, validating quality, handling edge cases
- Exploratory analysis (20%) — pulling distributions, finding signals, sanity checks
- Modeling / experimentation (15%) — fitting models, A/B test design, evaluation
- Communication (15%) — writeups, presentations, stakeholder calls
- Meetings (12%) — standups, partner syncs, exec readouts
- Production model engineering (5%) — deploying models (often co-owned with ML/SWE)
- Other (3%) — interviewing, hiring, training
The biggest behavioral difference: SWEs spend the day in the IDE; DSs spend the day in some mix of Jupyter, slide decks, and Slack threads. If communicating analysis to non-technical stakeholders sounds draining, SWE suits better. If reading legacy code to make a one-line fix sounds draining, DS suits better.
Education and Entry Path
SWE is the more credential-flexible role; DS still leans on master's-level training even when the work doesn't strictly require it.
| Path | SWE outcome | DS outcome |
|---|---|---|
| CS / SE bachelor's | Standard entry; FAANG-feasible | Possible at non-tech; tougher at FAANG without grad work |
| Bootcamp (well-regarded) | Possible; harder than 2018–2021 era | Rare; data analyst may be more accessible |
| Self-taught + portfolio | Possible at startups; harder at FAANG | Difficult; some success with strong Kaggle/research portfolio |
| Stats / quant master's | Adjacent path; some friction | Standard preferred entry |
| PhD (CS, Stats, Physics, Econ) | Specific to research-eng roles | Common at FAANG and quant; pay premium typical |
If you're already mid-career and choosing between bootcamp routes, SWE bootcamps have a clearer placement track than DS bootcamps in 2026. The DS market re-tightened in 2024–2025, and bootcamp-trained DS faces stronger filtering at hiring.
Switching Between Them
Both moves happen, but the difficulty is asymmetric.
SWE → DS
Lower friction. SWEs already have the engineering hygiene that helps with productionizing models; the gap is statistical reasoning, experimental design, and communication. Most SWEs add these via online courses + 6–12 months on a hybrid team or "data infra" role before transitioning fully. Typical timeline: 12–18 months part-time prep + 1 internal switch.
DS → SWE
Higher friction. DSs often lack systems-design experience, version control discipline at scale, and infrastructure depth. Bootcamp-style retraining isn't typically necessary for a competent DS; what's needed is sustained exposure to production code review, system-design study, and refactoring legacy code. Typical timeline: 18–24 months part-time prep + 1 lateral move to ML engineering before full SWE.
The intermediate role of ML Engineer (or "Applied Scientist" at some companies) is the standard bridge in either direction — it lets engineers do more modeling work and DSs do more production code, without requiring a clean-break transition.
Industry: Where Each Role Pays Best
Tech salaries dominate both roles, but the cross-industry picture differs.
- SWE — top industries by median: Software Publishing ($165K), Web Search Portals ($175K), Information Services ($150K), Computer Systems Design ($140K).
- DS — top industries by median: Software Publishing ($150K), Management of Companies ($135K), Insurance Carriers ($130K), Pharma R&D ($128K).
- SWE in non-tech: Healthcare IT ($110K), Banking IT ($120K), Government ($105K). Lower ceiling but stable.
- DS in non-tech: Healthcare ($95K), Retail Analytics ($105K), Government ($95K). Common but underpaid vs tech DS.
The "industry premium" matters more for DS than SWE. A senior SWE at a healthcare company earns 60–70% of a senior SWE at FAANG. A senior DS at a healthcare company earns 40–50% of a senior DS at FAANG. If maximum income is the goal, DS-in-tech is the only path that scales.
Methodology & Data Sources
Wage figures: BLS OES 15-1252 (Software Developers) and 15-2051 (Data Scientists), May 2024 release. Tech-industry total comp: Levels.fyi aggregated submissions 2024–2025, Blind comp threads (treated as directional only). Task breakdowns: O*NET task statements + practitioner surveys 2024 (Stack Overflow Developer Survey + Kaggle ML/DS Survey). Industry premiums: BLS OES industry-segmented filings May 2024. Bootcamp placement context: Council on Integrity in Results Reporting (CIRR) data + recent SWE/DS bootcamp first-job stats.
FAQ
- Does software engineering or data science pay more?
- Software engineering, by 10–25% at every comparable level. BLS OES May 2024 medians: SWE (15-1252) $132,270, DS (15-2051) $108,020. Top 10%: SWE $208,620 vs DS $184,090. The gap reflects deeper SWE talent supply, higher revenue attribution per SWE in most P&Ls, and DS roles being more often classified as cost-center.
- Is data science a dying field?
- No, but it has matured and contracted. The 2020–2023 DS hiring boom retracted in 2024–2025 as companies consolidated DS work into ML engineering and analytics engineering roles. Pure DS roles still exist but are more concentrated at well-funded tech and traditional analytics-rich industries (insurance, pharma, retail). New entrants face stronger filtering than 5 years ago. AI/ML engineering subspecialty is the hottest sub-niche in 2026.
- Should I become a software engineer or a data scientist?
- Pick by daily work, not pay. SWE: 60-70% of your day is in the IDE writing or reviewing code; you ship products. DS: 30-40% is data wrangling and 15-20% is communicating with stakeholders; you ship insights and experiments. If reading legacy code to make a one-line fix sounds draining, lean DS. If presenting analysis to non-technical stakeholders sounds draining, lean SWE.
- Can a software engineer switch to data science?
- Yes — and this is the easier of the two switches. SWEs already have engineering hygiene that helps with productionizing models; the gap is statistical reasoning, experimental design, and stakeholder communication. Most SWEs add these via online courses + 6–12 months on a hybrid team or 'data infra' role before transitioning. Typical timeline: 12–18 months part-time prep + 1 internal switch.
- Can a data scientist switch to software engineering?
- Yes, but harder than the reverse. DSs often lack systems-design experience, version control discipline at scale, and infrastructure depth. Bootcamp-style retraining isn't typically necessary for a competent DS; what's needed is sustained exposure to production code review, system-design study, and refactoring legacy code. Typical timeline: 18–24 months part-time prep + 1 lateral move to ML engineering before full SWE.
- What is an ML engineer compared to SWE and DS?
- ML Engineer (sometimes 'Applied Scientist' at Amazon, 'AI Engineer' at others) sits between the two — productionizes ML models, designs ML infrastructure, owns model deployment + monitoring. The role draws from both DS (modeling, statistical reasoning) and SWE (production code, system design, on-call). Pay typically tracks SWE bands at FAANG; the ML specialty often commands 5–15% above general SWE. ML Engineer is the standard bridge role in either DS↔SWE direction.
- Do data scientists earn more in tech vs non-tech industries?
- Significantly more. The DS-in-tech premium is much larger than the SWE-in-tech premium. A senior SWE at a healthcare company earns 60–70% of a senior SWE at FAANG; a senior DS at a healthcare company earns 40–50% of a senior DS at FAANG. If maximum income is the goal, DS-in-tech is the only DS path that scales to FAANG-comparable comp.
- Do you need a master's degree to be a data scientist?
- Not legally, but practically yes for FAANG-tier roles. Most large tech companies expect a stats/CS/quantitative master's or PhD for DS roles, though they'll occasionally accept exceptional bachelor's candidates with strong portfolios (Kaggle, papers, open-source). For non-tech DS roles, bachelor's + analytics experience is more common. SWE is much more credential-flexible — bootcamp and self-taught SWEs land at FAANG more often than bootcamp/self-taught DSs.
- How much does a senior data scientist make at FAANG?
- Total comp (base + equity at vest + bonus) $320K–$450K typical at L5 senior. Staff DS L6: $450K–$650K. Principal DS L7: $650K–$1M (rare; pay caps below SWE-equivalent levels at most companies). Non-tech senior DS: $130K–$170K. Sources: Levels.fyi aggregated submissions 2024–2025, treated as directional within ±15%.
- How much does a senior software engineer make at FAANG?
- Total comp $340K–$500K at L5 senior. Staff Engineer L6: $500K–$750K. Senior Staff / Principal L7: $750K–$1.2M. Distinguished Engineer L8: $1M–$2M+. Non-tech senior SWE: $140K–$180K. Pay typically exceeds DS at the same level by 10–25%.