Compensación
Market data- p25
- 154.225 US$
- median
- 200.750 US$
- p75
- 307.500 US$
Base anual, USD, según vacantes públicas · basado en n=120 · Actualizado septiembre de 2026. Cómo lo calculamos
Carrera y salario · data
Data Scientist
Datos de mercado para este puesto: salario, demanda y habilidades. Ver vacantes activas más abajo.
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.
Quiero este trabajo
Consigue trabajo como Data Scientist
Take the role-specific skills assessment, score instantly, and get matched to the companies hiring now.
Take the assessment →Free · ~12 min · no résumé needed
Estoy contratando
Contrata un Data Scientist verificado
Cada Data Scientist en diiirect pasa por una evaluación de habilidades y una revisión humana. Contrata directo: sin agencias, sin intermediarios.
Publica un puesto →Publica en minutos · solo candidatos verificados y evaluados
Sobre el puesto
Qué hace un Data Scientist
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 $202,500 median salary reflect a mature, competitive market. There's real demand, but the bar for entry is higher than it was five years ago. Candidates who combine technical depth, statistical rigor, and business communication will find strong opportunities. Companies that hire thoughtfully and invest in their data science teams will build sustainable competitive advantages.
Por seniority · mediana global
Intern30.246 US$
Junior49.149 US$+18.903 US$
Mid277.500 US$+228.351 US$
Senior102.079 US$
Staff128.544 US$+26.465 US$
Principal158.789 US$+30.246 US$
Director181.474 US$+22.684 US$
VP226.842 US$+45.368 US$
C-level302.456 US$+75.614 US$
Where would you land?
~50th percentile — about 200.750 US$ would out-earn an estimated 50% of Data Scientists.
Estimated from the p25–p75 band. For orientation, not an offer.
Vacantes abiertas
Puestos de Data Scientist en vivo
Las vacantes más recientes registradas en el mercado. Postúlate directamente, sin intermediarios.
- Lead Data ScientistCiscoChicago, Cook Countyhoy
- Sr. Scientist (Self Care/otc) - (Fixed Term 12 Months)KenvueSão José dos Campos, Estado de São Paulohoy
- Senior Data ScientistMongoDBCork, Ireland; Dublin, Irelandhoy
- Data Science Intern, Algorithms (Summer 2027)LyftToronto, Canadahoy
- Data Science Intern, Algorithms (Summer 2027)LyftNew York, NYhoy
- Data Science Intern, Algorithms (Summer 2027)LyftSan Francisco, CAhoy
- Data Science(Gen AI/LLM) Manager job opening at GlobalDataGlobalData Publications IncHyderabad, Telanganahoy
- Data Science(Gen AI & LLM) - Lead/Sr Engineer job openingGlobalData Publications IncHyderabad, Telanganahoy
- Senior Data ScientistAlpacaRemote - AmericasRemotoayer
- Data Scientist (Ds)Adzuna · BR · project managerSão Paulo, Estado de São Pauloayer
- R&D Scientist - Power System ProtectionHitachiBengaluru, Karnataka, Indiahace 2 d
- Data ScientistLyftSeattle, WAhace 2 d
Vacantes en otras empresas
Vacantes activas de Data Scientist
Puestos de otras empresas que están contratando Data Scientist ahora mismo, tomados directamente de sus propios portales de empleo.
- Senior Data ScientistMongoDBCork, Ireland; Dublin, Ireland
- Data Science Intern, Algorithms (Summer 2027)LyftToronto, Canada
- Data Science Intern, Algorithms (Summer 2027)LyftNew York, NY
- Data Science Intern, Algorithms (Summer 2027)LyftSan Francisco, CA
- Senior Data ScientistAlpacaRemote - Americas
- R&D Scientist - Power System ProtectionHitachiBengaluru, Karnataka, India
- Data ScientistLyftSeattle, WA
- Data Science InternCoinbaseHybrid - San Francisco, CA
- Staff Applied ScientistLyftSan Francisco, CA
- Sr.Data Scientist ISamsaraBengaluru - BLR1
Trayectorias afines
Puestos relacionados
Dónde se paga mejor
Compensación por región
Base anual mediana en USD por región. Contratar globalmente amplía el rango del que puedes reclutar, y el arbitraje que puedes capturar.
Regiónp25Medianap75Fuente
US120.000 US$140.000 US$140.000 US$Market data
CA75.290 US$88.576 US$104.520 US$Estimated
GB71.617 US$84.256 US$99.422 US$Estimated
UY40.399 US$47.529 US$56.084 US$Estimated
MX38.563 US$45.368 US$53.535 US$Estimated
BR36.727 US$43.208 US$50.985 US$Estimated
CL36.727 US$43.208 US$50.985 US$Estimated
CR36.727 US$43.208 US$50.985 US$Estimated
AR31.218 US$36.727 US$43.338 US$Estimated
CO31.218 US$36.727 US$43.338 US$Estimated
PE29.381 US$34.566 US$40.788 US$Estimated
Bueno saberlo
Preguntas frecuentes
What's the salary range for a Data Scientist?
The median Data Scientist salary is $200,750. Most Data Scientist roles pay between $154,225 and $307,500 (USD, annual), based on 120 salary observations.
Which companies hire Data Scientists?
Companies actively hiring Data Scientists include Lyft, OpenAI, Reddit, Anthropic, Spotify, Datadog.
How do I get hired as a Data Scientist on diiirect?
Apply directly to 261 open Data Scientist postings on diiirect — no recruiters, no middle layer. Companies review your profile and hire you directly.
Kit de contratación
Plantillas para contratar Data Scientist
¿Buscas trabajo de Data Scientist?
Postúlate directamente a las mejores empresas en diiirect. Sin intermediarios.
Las cifras de salario y demanda se derivan de vacantes públicas y de la línea base BLS OEWS. Lee cómo lo calculamos.