💻
Technology & Software
Google, Microsoft, Amazon
Data Scientists
Data scientists turn messy business data into forecasts, experiments, and recommendations that help teams decide what to build, buy, or fix. The job is distinct because it blends coding, statistics, and plain-language storytelling, so you spend as much time framing the right question and explaining the answer as you do building models. The tradeoff is that the pay is strong, but the work depends on imperfect data and skeptical stakeholders who may not agree with the results.
Also known as Applied ScientistDecision ScientistMachine Learning ScientistResearch Data ScientistData Science Analyst
Median Salary
$112,590
Mean $124,590
U.S. Workforce
~233K
23.4K openings per year
10-Year Growth
+33.5%
245.9K to 328.3K
Entry Education
Bachelor's degree
+ None experience
What This Role Looks Like in Practice
Data Scientists sits in the Technology category. In practical terms, this role combines day-to-day execution, cross-team coordination, and consistent decision-making under real business constraints.
U.S. employment is currently about ~233K workers, with a median annual pay of $112,590 and roughly 23.4K openings each year. Based on BLS projections, total employment is expected to grow from 245.9 K in 2024 to 328.3K in 2034.
Most hiring paths start with Bachelor's degree in a quantitative field, and employers typically expect none of related experience. Many careers in this track begin around Data Analyst and can progress toward Data Science Manager. High-value skills usually include Critical Thinking, Reading Comprehension, and Python, R & Jupyter Notebooks, paired with soft skills such as Active Listening, Speaking, and Writing.
Core Responsibilities
- Pull data from internal systems, public reports, and outside vendors so it can be used for analysis.
- Study product, market, and technology trends to find new opportunities or risks for the business.
- Compare company results with competitors and industry benchmarks to spot where performance is changing.
- Check analyses and data outputs for mistakes, gaps, or inconsistent numbers before anyone acts on them.
Keep exploring: more Technology careers or browse all job titles.
A Day in the Life
01 Pull data from internal systems, public reports, and outside vendors so it can be used for analysis.
02 Study product, market, and technology trends to find new opportunities or risks for the business.
03 Compare company results with competitors and industry benchmarks to spot where performance is changing.
04 Check analyses and data outputs for mistakes, gaps, or inconsistent numbers before anyone acts on them.
05 Write clear requirements for dashboards, reports, and other analytics tools so they can be built correctly.
06 Turn findings into reports or presentations that managers, executives, clients, and other teams can understand.
Industries That Hire
💳
Finance & Fintech
JPMorgan Chase, Capital One, Stripe
🏥
Healthcare & Biotech
UnitedHealth Group, Pfizer, Moderna
🛒
Retail & E-commerce
Walmart, Target, Shopify
📊
Consulting & Professional Services
Deloitte, Accenture, McKinsey
Pros and Cons
Advantages
+
The pay is strong, with a mean annual wage of $124,590 and a median of $112,590.
+
The job outlook is solid, with 33.5% projected growth and about 23.4K annual openings.
+
You can enter without prior work experience or on-the-job training, so a strong portfolio can matter a lot.
+
The work transfers across many industries, from tech and finance to healthcare and retail.
+
Your analysis can directly shape product choices, pricing, forecasting, and other business decisions.
Challenges
-
A bachelor's degree is the norm, and 76.59% of workers have one, so the field is still fairly credential-driven.
-
Many employers also prefer a master's degree, which adds another 13.87% of workers to the competitive pool.
-
A lot of the job is cleaning messy data, writing specs, and explaining results, not just building exciting models.
-
Routine reporting and basic analysis can be automated or pushed into self-service tools, which can limit simpler entry-level work.
-
Long-term growth often means moving into lead or management roles because individual-contributor ladders can narrow at the top.
Career Progression
Data Analyst
0-2 years
$65K-$85K
Builds basic reports, cleans data, and learns how business questions turn into measurable metrics.
Data Scientist
2-5 years
$90K-$125K
Works on predictions, experiments, and analysis that guide product and business decisions.
Senior Data Scientist THIS ROLE
5-8 years
$130K-$165K
Owns harder problems, reviews other people's work, and partners directly with leaders on high-impact questions.
Principal Data Scientist
8-12 years
$170K-$205K
Sets the analytic approach for major projects and tackles ambiguous problems that cut across teams.
Data Science Manager
10+ years
$210K-$250K
Leads a team, sets priorities, and balances people management with technical decision-making.
Education Paths
Bachelor's degree in a quantitative field
This is the usual entry point, and 76.59% of workers in the occupation report a bachelor's degree.
Master's degree in data science, statistics, computer science, or a related field
A graduate degree can help for more advanced modeling work and is held by 13.87% of workers in this role.
Post-baccalaureate certificate in analytics or machine learning
This is a smaller but realistic path for career changers, and 4.78% of workers report a post-baccalaureate certificate.
Associate's degree or other 2-year degree plus a strong portfolio
A less common route, held by 4.78% of workers, though most employers still prefer more formal training.
Key Skills
Technical
Critical Thinking 79%
Reading Comprehension 79%
Python, R & Jupyter Notebooks 78%
SQL & Relational Databases 77%
Machine Learning & Scikit-Learn 76%
Tableau, Power BI & Looker 75%
Mathematics 70%
Complex Problem Solving 69%
Statistics, A/B Testing & Experiment Design 68%
Data Cleaning, ETL & dbt 67%
Soft Skills
Active ListeningSpeakingWritingCollaborationCuriosityTime Management Job Outlook and Trends
+33.5%
Projected growth (2024-2034)
23.4K
Annual job openings
Employment is projected to rise from 245.9K to 328.3 K over the next decade, representing 33.5% growth. Around 23.4 K openings per year include both newly created roles and replacement hiring from turnover.
Remote availability is currently High availability. Demand remains strongest where employers need practical domain knowledge plus modern workflow and data skills.