Compensation
Market data- p25
- $154,000
- median
- $197,702
- p75
- $307,500
Annual base, USD, from public postings · based on n=125 · Updated September 2026. How we calculate this
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Career & pay · data
Data Scientist
Market data for this role — salary, demand, and skills. See live openings below.
Data scientists command a median salary of $202,500 across 248 active postings at 97 companies, with roles spanning analytics, machine learning, and statistical modeling. Demand remains strong for candidates who can translate data into business decisions.
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About the role
What a Data Scientist does
What data scientists actually do
Data scientists sit at the intersection of statistics, engineering, and business strategy. Day-to-day work varies by company and seniority, but the core mandate is consistent: extract signal from noise, build models that predict or classify, and communicate findings to non-technical stakeholders.
In practice, this means spending time on data exploration and cleaning (often 40–60% of the job), building and validating statistical or machine learning models, and then operationalizing those models so they drive real decisions. A data scientist at a fintech company might build fraud detection systems; at a retailer, demand forecasting models; at a healthcare firm, patient risk stratification. The domain changes, but the workflow—hypothesis, data, model, validation, deployment—stays constant.
Unlike data engineers (who build pipelines) or analytics engineers (who focus on dashboards and BI), data scientists are expected to own the full lifecycle from raw data to business impact. That means you need to be comfortable with ambiguity, statistical rigor, and the messiness of real-world datasets.
The compensation picture
Data scientists earn a median of **$202,500** annually. The 25th percentile sits at **$154,075**, while the 75th percentile reaches **$308,625**. This spread reflects real variation based on experience, location, company size, and specialization.
Entry-level data scientists (0–2 years) typically land near the p25 range, often in smaller markets or startups. Mid-career practitioners (3–7 years) cluster around the median, especially in tech hubs and at established companies. Senior data scientists and those with specialized expertise in areas like deep learning or causal inference command the p75 and above.
The median of $202,500 is notably higher than many adjacent roles—it reflects both the technical bar and the business value these roles generate. Companies hire data scientists to reduce risk, optimize operations, or unlock new revenue streams. When a model saves millions in fraud losses or improves conversion by 2%, the ROI on a $200k+ salary is clear.
Hiring demand and market dynamics
There are **248 active postings** across **97 companies** for data scientist roles right now. That's a healthy, competitive market. The fact that 248 openings span 97 companies (roughly 2.5 openings per company) suggests that larger tech firms and data-driven enterprises are hiring multiple data scientists, while mid-market and smaller companies are adding their first or second.
Demand has remained resilient even as the broader tech market has cooled. This is partly because data science is no longer a "nice to have"—it's embedded in product development, risk management, and operations at most growth-stage companies. It's also because the supply of truly capable data scientists hasn't kept pace with demand. Many people can write Python; fewer can design experiments, validate models rigorously, and communicate uncertainty to executives.
Skills that matter
The technical foundation is non-negotiable: Python or R, SQL, statistics, and machine learning fundamentals. But the candidates who get hired and promoted are those who combine technical depth with business acumen and communication.
**Statistical thinking** is underrated. You need to understand p-values, confidence intervals, experimental design, and the difference between correlation and causation. Many data scientists can train a model; fewer can tell you whether the results are statistically significant or just noise.
**SQL and data manipulation** remain essential. Most data lives in databases or data warehouses. If you can't write efficient queries or wrangle messy data, you'll spend weeks waiting for engineers or drowning in slow code.
**Machine learning frameworks** matter, but they're tools. Knowing scikit-learn, TensorFlow, or XGBoost is useful, but understanding when to use a simple linear regression versus a neural network is more valuable. The best data scientists pick the simplest model that solves the problem.
**Communication** is often the difference between a data scientist who influences strategy and one who publishes reports no one reads. You'll spend significant time translating statistical findings into business language, defending assumptions, and pushing back on bad requests. If you can't explain why a model works or why it fails, the model doesn't matter.
**Domain knowledge** accelerates impact. A data scientist who understands healthcare regulations, financial risk, or supply chain logistics can ask better questions and avoid costly mistakes. This often comes from prior experience or deep curiosity about the industry you're joining.
How to get hired
Companies hiring data scientists are looking for a few signals:
**A portfolio of real work.** GitHub projects, Kaggle competitions, or published analyses show you can execute end-to-end. Bonus points if you can explain your reasoning and discuss what you'd do differently.
**Evidence of statistical rigor.** In interviews, be prepared to discuss experimental design, how you'd validate a model, and how you'd communicate uncertainty. Many candidates oversell their models; hiring managers want to see intellectual honesty.
**Domain-specific knowledge or curiosity.** If you're interviewing at a healthcare startup, having worked in healthcare or having read deeply about the space matters. If you're joining a fintech firm, understanding financial products or risk is a plus.
**Collaboration and communication.** Technical interviews often include a take-home project or a case study where you present findings. Use this to show you can explain your work clearly, defend your choices, and acknowledge limitations.
**Willingness to own ambiguity.** Data science roles often start with vague problems: "How do we reduce churn?" or "What's the best pricing strategy?" Interviewers want to see that you can ask clarifying questions, scope the problem, and iterate.
How to hire well
If you're building a data science team, a few principles help:
**Hire for statistical thinking first, tools second.** A strong statistician can learn Python or R. A Python expert without statistical rigor will build models that look good but fail in production.
**Test for communication.** Include a presentation component in your interview. Ask candidates to explain a past project to a non-technical audience or to critique a flawed analysis. This reveals whether they can actually influence decisions.
**Be clear on what you need.** "Data scientist" is a broad title. Do you need someone to build real-time recommendation systems (closer to ML engineering)? Or someone to analyze A/B tests and inform product strategy (closer to analytics)? Hiring for the wrong profile wastes everyone's time.
**Invest in onboarding.** Data scientists are most productive when they understand your data infrastructure, business metrics, and decision-making process. Spending a few weeks on this upfront pays dividends.
**Evaluate for impact, not activity.** It's tempting to measure data scientists by models built or analyses completed. Instead, ask: Did this work change a decision? Did it save money or improve a metric? This keeps the team focused on business value.
The market outlook
Data science roles are unlikely to disappear. As companies collect more data and competition intensifies, the ability to extract insights and build predictive models becomes more valuable, not less. That said, the role is evolving. AutoML and LLMs are automating routine tasks, which means data scientists need to move upstream—toward problem framing and business strategy—rather than downstream toward model training.
The 248 active postings and $202,500 median salary reflect a mature, competitive market. There's real demand, but the bar for entry is higher than it was five years ago. Candidates who combine technical depth, statistical rigor, and business communication will find strong opportunities. Companies that hire thoughtfully and invest in their data science teams will build sustainable competitive advantages.
By seniority · global median
Intern$30,246
Junior$49,149+$18,903
Mid$277,500+$228,351
Senior$102,079
Staff$128,544+$26,465
Principal$158,789+$30,246
Director$181,474+$22,684
VP$226,842+$45,368
C-level$302,456+$75,614
Where would you land?
~50th percentile — about $197,702 would out-earn an estimated 50% of Data Scientists.
Estimated from the p25–p75 band. For orientation, not an offer.
Open positions
Live Data Scientist roles
The most recent openings tracked across the market. Apply directly — no middle layer.
- Senior Data ScientistAltamira TechnologiesCamp Lejeune, Onslow Countytoday
- Data Scientist IIPODSPinellas Park, Pinellas Countytoday
- Data ScientistHelp at HomePierre, Hughes Countytoday
- Data ScientistHelp at HomeProvidence, Providence Countytoday
- Data ScientistHelp at HomeBismarck, Burleigh Countytoday
- Senior Data ScientistBMOToronto, Ontariotoday
- Senior Data ScientistBMOToronto, Ontariotoday
- Data Science Intern (2027)FigmaSan Francisco, CA; New York, NYtoday
- Data Scientist, RiskgustoSan Francisco, CA - Hybridtoday
- Senior Data Scientist - Decision IntelligenceXanaduToronto, Ontariotoday
- Senior Data Scientist - Decision IntelligenceXanaduToronto, Ontariotoday
- Senior Data ScientistSonatypeToronto, Ontariotoday
Openings elsewhere
Live Data Scientist openings
Roles from other companies hiring Data Scientists right now, sourced straight from their own job boards.
- Data Science Intern (2027)FigmaSan Francisco, CA; New York, NY
- Data Scientist, RiskgustoSan Francisco, CA - Hybrid
- Senior Staff Data Scientist, Guest & Host Marketplace AIAirbnbRemote
- Staff Data Scientist (Armis)ServiceNowTel Aviv, IL
- Forward Deployed Data ScientistintercomSan Francisco, California
- Senior Data Scientist - IA Générative F/HDevoteamLyon, Auvergne-Rhône-Alpes, FR
- Staff Data Scientist - Ads Measurement, Signals, PrivacyRedditToronto, Canada
- Sr. Staff Data Scientist - Ads Measurement, Signals, PrivacyRedditToronto, Canada
- Sr. Staff Data Scientist - Ads Measurement, Signals, PrivacyRedditRemote (United States)
- Staff Data Scientist - Ads Measurement, Signals, PrivacyRedditRemote (United States)
Adjacent paths
Related roles
Where it pays
Compensation by region
Median annual base in USD by region. Hiring globally widens the band you can recruit from — and the arbitrage you can capture.
Regionp25Medianp75Source
US$120,000$140,000$140,000Market data
CA$75,290$88,576$104,520Estimated
GB$71,617$84,256$99,422Estimated
UY$40,399$47,529$56,084Estimated
MX$38,563$45,368$53,535Estimated
BR$36,727$43,208$50,985Estimated
CL$36,727$43,208$50,985Estimated
CR$36,727$43,208$50,985Estimated
AR$31,218$36,727$43,338Estimated
CO$31,218$36,727$43,338Estimated
PE$29,381$34,566$40,788Estimated
Good to know
Frequently asked
What's the salary range for a Data Scientist?
The median Data Scientist salary is $197,702. Most Data Scientist roles pay between $154,000 and $307,500 (USD, annual), based on 125 salary observations.
Which companies hire Data Scientists?
Companies actively hiring Data Scientists include OpenAI, Reddit, Lyft, Anthropic, Airbnb, Stripe.
How do I get hired as a Data Scientist on diiirect?
Apply directly to 265 open Data Scientist postings on diiirect — no recruiters, no middle layer. Companies review your profile and hire you directly.
Hiring kit
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Salary and demand figures are derived from public job postings and the BLS OEWS baseline. Read how we calculate this.