Profession · Full profile

Data Scientist

Data scientists collect data, build statistical models, and explain insights to guide business choices.

Average salary

$120,230

Salary range

$67,240 – $199,130

Training time

4 years full time

Job market

Much faster than average

9/10

Work-life balance

Good

7/10

Data scientists turn complex datasets into actionable insights. They collect, clean, and combine information from multiple sources to build and test statistical and machine learning models. Working closely with engineers, they help deploy models into production systems. They also create reports and visual dashboards to share clear findings with business teams. Most professionals work daytime full time hours in computer-based office settings, often enjoying flexible remote options while handling deadline demands across technology, healthcare, finance, consulting, and government sectors.

A workday

A day in the life

What does a workday really look like?

A standard day centered on preparing data, training statistical models, and evaluating results.

8:30 AM
Check emails and task boards
Review daily priorities and messages from project teams.
9:00 AM
Clean and combine data
Query databases with SQL and prepare datasets for analysis.
11:00 AM
Team sync
Meet with business partners to discuss modeling goals.
12:00 PM
Lunch break
Midday meal and rest away from the screen.
1:00 PM
Build machine learning models
Write code in Python or R to train statistical models.
3:00 PM
Test model accuracy
Evaluate test runs and refine parameters.
4:30 PM
Document workflow
Log results and update project documentation before logging off.

Task breakdown

A collaborative day focused on engineering handoffs, live testing, and business reporting.

8:30 AM
Morning preparation
Review dashboard metrics and prepare slide decks.
9:30 AM
Engineering sync
Meet with software engineers to discuss deploying models into production.
11:00 AM
Model testing in production
Check the health and performance of live model feeds.
12:30 PM
Lunch break
Take a break from computer work.
1:30 PM
Business presentation
Explain analytic findings and dashboards to non-technical business leaders.
3:00 PM
Refine visual reports
Adjust data visualizations based on feedback from business teams.
4:30 PM
Project wrap-up
Organize code repositories and plan next steps for the week.

Salary data

What do you really earn?

Salary by experience, sector and region.

Minimum

$67,240

Average

$120,230

Maximum

$199,130

Salary by region

Region	Min	Avg.	Max
National	$67,240	$120,230	$199,130
California	$77,480	$141,590	$224,920
Texas	$64,540	$122,090	$170,780
New York	$73,330	$130,460	$214,080
Pennsylvania	$63,340	$106,850	$162,290
North Carolina	$65,970	$119,090	$172,830

Typical benefits

Remote work

Access to flexible hybrid schedules or fully remote work setups.

Stock options at technology companies

Company equity grants offered alongside standard baseline compensation.

Conference and training budget

Dedicated funding for technical education, professional development, and industry conferences.

Health insurance and 401(k)

Comprehensive medical coverage and company-sponsored retirement savings plans.

Pathways

Education pathways

Which paths lead to this profession?

Bachelor's degree

4 years full time

Bachelor's degree in mathematics, statistics, computer science or a related field

The minimum for most data scientist jobs.

Costs: About $11,950 a year in average published tuition and fees at a public four-year college for in-state students, and $45,000 at a private nonprofit four-year college
Entry requirements: High school diploma or equivalent
High school subjects: Mathematics, statistics, computer science

Master's degree

1 to 2 years full time

Master's degree in data science, statistics or computer science

Many employers prefer a master's degree or higher.

Entry requirements: Bachelor's degree with math and programming
High school subjects: Data science, statistics, computer science

Profile

Skills & personality

Is this a good fit for you?

Hard skills

Python or RWriting code to clean data, perform statistical analyses, and train models.
SQLQuerying and extracting structured records from database systems.
StatisticsApplying mathematical principles to analyze datasets and measure accuracy.
Machine learningBuilding and tuning predictive models using modern algorithms.
Data visualizationCreating dashboards and charts to communicate results clearly.
Cloud data platformsManaging data resources and executing computing tasks in cloud environments.

Soft skills

CuriosityExploring datasets to uncover underlying trends and business opportunities.
Explaining complex results simplyTranslating intricate technical findings for non-technical stakeholders.
Critical thinkingEvaluating model behavior and data quality with careful skepticism.
Business senseAligning data projects with commercial needs and organizational objectives.

Is this a good fit for you?

✓
Analytical mindsetApproaches complex business questions with structured logic and statistical rigor.
✓
Detail orientationPays close attention to data cleanliness, code quality, and testing procedures.
✓
AdaptabilityAdjusts smoothly as project requirements, organizational priorities, and tools shift.
✓
PersistenceWorks patiently through messy raw data, edge cases, and stubborn software bugs.
✓
CollaborationWorks effectively across multidisciplinary teams of engineers and business managers.

Market

Job market & outlook

What does the market look like?

Openings per year

24,800

Trend

Growth

+34.6%

AI impact

5/10

AI tools write code and speed up analysis, while framing the right question and judging results stay with people.

Regional demand

New York

Strong demand centered on financial firms, insurance organizations, and consulting agencies.

7/10

North Carolina

Regular recruitment supported by technology centers, clinical research, and financial institutions.

7/10

Pennsylvania

Consistent needs within healthcare organizations, pharmaceutical groups, and corporate offices.

7/10

California

Active employment market driven by large technology centers and corporate offices.

6/10

Texas

Steady hiring across corporate branches, technology operations, and financial services.

5/10

Growth

Career paths

Where can this profession take you?

Vertical growth

Junior data scientist
0-2 years
Focuses on data cleaning, basic model development, and preliminary analysis under supervision.
Senior data scientist
3-6 years
Directs modeling projects, optimizes algorithmic performance, and collaborates with production engineers.
Lead data scientist or data science manager
6+ years
Guides technical strategy, manages analytical personnel, and aligns projects with executive goals.

Horizontal move

Software developer
Branches horizontally into building application software and core engineering infrastructure.
View Software Developer
Business analyst
Transitions horizontally to concentrate on business requirements, operations, and organizational metrics.
View Business Analyst

Good to know

Practical info

Working hours: Full time; deadlines can mean extra hours.
Remote work: Very common: many roles are hybrid or fully remote.
Physical requirements: Office work at a computer.
Typical employers: Technology companies, finance and insurance companies, consulting firms, healthcare and pharmaceutical companies, government agencies.
Professional associations: American Statistical Association

FAQ

Frequently asked questions

What does a data scientist do?

A data scientist analyzes data to help organizations solve complex problems. They collect, clean, and combine datasets from various sources, then construct statistical and machine learning models. After evaluating model performance, they create reports or dashboards and explain findings to business leaders. They also partner with engineering teams to bring successful models into active production.

Is remote work common for data scientists?

Remote work is very common in this profession, with many positions offered as hybrid or fully remote. Because the work primarily involves office tasks on a computer, data scientists can easily access cloud data platforms and write code from home. However, teams often coordinate online to discuss modeling strategies and present results.

What degree is needed to become a data scientist?

A bachelor's degree in mathematics, statistics, computer science, or a related field is the minimum requirement for most jobs. In addition, many employers prefer applicants who hold a master's degree or higher in data science, statistics, or computer science. Solid training in both mathematics and computer programming is essential for entering the field.

How does artificial intelligence affect data science work?

Artificial intelligence tools speed up routine coding and assist with technical data analysis. While these automated tools handle repetitive programming tasks, human data scientists remain essential for framing the right business questions and critically judging statistical results. The profession relies heavily on human critical thinking and business context to ensure models remain valid.

What are the typical working hours for a data scientist?

Data scientists generally work standard full time hours during the day. Most positions follow standard office schedules without night shifts. However, tight project deadlines or upcoming production deployments can sometimes require working extra hours. The overall work environment is flexible, but project peaks create occasional periods of increased workload.

What technical skills are most important for data scientists?

The core technical skills include programming in Python or R, database querying with SQL, statistics, and machine learning. In addition, practitioners must know how to build data visualizations and operate within cloud data platforms. Combining these coding capabilities with sound mathematical principles allows professionals to extract meaningful insights from large data collections.

What industries employ data scientists?

Data scientists work across technology companies, finance and insurance firms, consulting agencies, healthcare and pharmaceutical organizations, and government agencies. Almost any sector that collects large amounts of digital information hires data scientists to interpret findings. This wide range of employers provides diverse career paths and specialized application areas.

Related professions

In depth

All about data scientist

The background, context and details.

What data scientists do

Data scientists collect, clean, and combine data from multiple sources to solve practical organizational problems. They spend substantial time preparing raw records, writing queries in SQL, and exploring trends. Once data is ready, they build statistical and machine learning models to identify patterns and predict future outcomes. They test these models, measure their accuracy, and partner with software engineers to deploy algorithms into active production. In addition to technical execution, data scientists translate analytical outputs into dashboards and clear reports, helping business teams understand the practical implications of findings.

Essential tools and technical skills

Succeeding as a data scientist requires a solid foundation in programming, mathematics, and systems design. Writing code in Python or R is fundamental for manipulating data and running predictive models. Data professionals rely on statistics to validate findings and avoid misleading conclusions. They also use cloud data platforms to process large amounts of information and design intuitive data visualizations to present results. Alongside technical proficiency, soft skills like critical thinking, business sense, curiosity, and the ability to explain complex results simply are vital for translating math into corporate action.

Typical work settings and schedules

Most data scientists work full time in computer-based office environments during regular day shifts. Flexible schedules and remote arrangements are very common, allowing professionals to work in hybrid or fully home-based setups. While day-to-day operations offer strong balance, project deadlines can bring temporary peaks with extra hours. Typical employers include technology companies, finance and insurance companies, consulting firms, healthcare and pharmaceutical companies, and government agencies.

Education and career growth

Entering the data science field usually requires a bachelor's degree in mathematics, statistics, computer science, or a related field. Many employers prefer candidates with a master's degree in data science, statistics, or computer science. Career progression often begins at the junior data scientist level before advancing to senior data scientist roles and eventually lead data scientist or data science manager positions. Professionals can also transition sideways into related technology roles such as software developer or business analyst.