👩💼 What does a statistician actually do on a day-to-day basis?

Statisticians collect, analyze, and interpret data to help organizations make informed decisions. Their daily tasks include designing studies, cleaning datasets, running statistical models, presenting results, and advising teams on using data effectively.

🎓 What qualifications do you need to become a statistician?

Most statisticians begin with a bachelor’s degree in statistics, mathematics, or data science. Many employers prefer or require a master’s degree for technical roles, while research or academic positions may require a PhD.

What Is a Statistician? Roles, Skills, and Career Path

Without data, you're just another person with an opinion.
W. Edwards Deming,American composer and economist

Statisticians are central to most industries today. By turning data into insights, they guide decisions across many industries. Here's precisely what they do, where they work, and the skills they need.

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What Does a Statistician Do?

Using mathematical and analytical techniques to interpret data, statisticians solve problems and guide decisions. Through a combination of quantitative reasoning, statistical modeling, and practical problem-solving, they answer real questions across fields such as healthcare, finance, government, tech, and scientific research. Each job varies by sector, but most statisticians combine technical expertise and communication skills to turn data into insights.

Website analytics on a laptop screen. — Statisticians need to understand data and communicate it to people. | Photo by Carlos Muza

Core Responsibilities

Your typical statisticians will design studies, analyze data, and interpret results. They take complex information and turn it into conclusions, developing methods to improve accuracy and ensure findings can be communicated clearly. While different industries will ask for other things of their statisticians, here are some of the most common responsibilities.

Designing Studies and Experiments: Creating surveys, experiments, or data collection plans that produce reliable and valid datasets.

Analyzing and Interpreting Data: Applying statistical tests, developing models, and identifying meaningful patterns or trends.

Evaluating Uncertainty and Risk: Measuring variability, confidence, and potential errors to help decision-makers understand the reliability of results.

Building Predictive Models: Developing forecasting tools and statistical models that predict outcomes or simulate scenarios.

Communicating Findings: Presenting results to technical and non-technical audiences through reports, presentations, dashboards, and visualizations.

Ensuring Data Quality: Cleaning datasets, validating inputs, and checking for inconsistencies or biases before conducting analysis.

Supporting Decision-Making: Contributing to strategic, scientific, financial, or operational decisions using evidence-based insights.

Tools and Techniques

Most statisticians use computational tools, mathematical methods, and analytical techniques. They use tools to clean, organise, model, visualize, and interpret information. The exact toolkit will vary by industry and role, but here are some common ones.

Statistical Software and Programming Languages: Tools such as R, Python, SAS, Stata, and SQL are central to statistical work. Statisticians use them to manage datasets, run statistical tests, build models, automate workflows, and create reproducible analyses. Many industries, such as pharmaceuticals, government, and research, rely on validated or regulated tools like SAS for reporting and modeling.

Experimental Design and Data Collection: Statisticians use techniques such as randomised experiments, sampling strategies, survey design, and controlled studies to ensure the data they collect is reliable. By choosing the right experimental structure, they minimize bias, improve accuracy, and produce datasets suitable for valid inference.

Modeling and Predictive Analysis: Common methods include regression analysis, probability modelling, time-series forecasting, classification models, and simulation techniques. Statisticians can use these methods to explore relationships, predict outcomes, and quantify uncertainty in decision-making.

Data Cleaning and Validation: Real-world datasets often contain missing values, inconsistencies, or errors. Statisticians apply cleaning techniques to standardise data, validate inputs, and prepare datasets for modeling. This is essential for ensuring accuracy, reliability, and transparency in the final results.

Visualization and Reporting: Statisticians present insights using charts, dashboards, written reports, and interactive visual tools. Visualisation helps stakeholders quickly understand complex findings, compare scenarios, and make informed decisions, regardless of their technical background.

How to Become a Statistician

Every statistician needs a strong foundation in mathematics and is good at analytical thinking and statistical methodology. They mostly gain these skills through formal education and then with practical experience. The exact route can vary, depending on your goals and preferred learning approach.

Financial data on a computer screen. — Before you can start working with stats, you need to study. | Photo by m.

Educational Pathways

Most students will complete an undergraduate degree majoring in statistics. This is where budding statisticians lay the foundations for their mathematical and analytical skills. Once they have these, they can progress to more specialized studies. Once they earn a bachelor's degree, statisticians can find entry-level positions. Still, many employers prefer a master's degree for research-intensive fields such as healthcare, government, and finance.

Bachelor’s Degrees and Foundational Study: Most statisticians start with a bachelor’s degree in statistics, mathematics, data science, or a related quantitative field. These programs cover probability, calculus, linear algebra, regression, and introductory statistical computing, helping students build the mathematical fluency needed for advanced analysis.

Master’s Degrees and Advanced Preparation: A master’s degree is widely considered the professional standard for many statistician roles. Graduate programs include coursework in multivariate analysis, sampling, Bayesian modeling, statistical programming, and domain-specific applications. This level of study prepares students for technical industry roles, government research, and applied analytics.

PhD Pathways for Research Careers: Students interested in academic research, advanced methodological development, or high-level analytical consulting may pursue a doctoral degree. PhD programs involve original research, teaching experience, and deep theoretical study, qualifying graduates for senior research or faculty positions.

Additional Coursework and Electives: Many aspiring statisticians strengthen their profiles by taking courses in computer science, machine learning, economics, biology, or engineering. These electives help bridge the gap between statistical theory and real-world application.

Gaining Experience

To become a statistician, experience is essential. Employers want skilled graduates, but they also want people who can take what they learned in class and apply it to real-world problems. Luckily, there are opportunities to gain experience both before and after graduation.

Internships and Applied Projects: Internships in government agencies, healthcare organisations, finance, technology, or research labs allow students to work on real datasets and contribute to active projects. These experiences strengthen technical skills and help students explore which industries suit their interests.

Capstone and Research Opportunities: Many undergraduate and master’s programs require a capstone project or independent research study. These projects often involve solving applied problems, collaborating with faculty, and presenting formal results, all of which mirror professional statistical work.

Statistical Consulting and Campus Labs: Some universities offer consulting centerq where students assist with data analysis for external clients or academic departments. This experience teaches students how to interpret messy datasets, justify methodological choices, and communicate findings clearly.

Independent and Portfolio Projects: Students can build a portfolio by analyzing public datasets, contributing to open-source projects, or completing online challenges. A strong portfolio shows employers practical ability, initiative, and technical curiosity.

Professional Development

Your long-term growth as a statistician depends on your professional development. You have to stay up to date with new tools, methods, and industry expectations. Make sure you expand your expertise, specialize, and adapt to emerging technologies and trends. With continuous development, you can enhance your career mobility, opening doors to higher-level roles in research, leadership, and advanced analytics.

People looking at analytics during a meeting. — While stats will be your main focus for professional development, don't underestimate the power of soft skills like communication. | Photo by UX Indonesia

Continuing Education and Certificates: Many statisticians take additional courses in areas such as statistical programming, data science, machine learning, survey methodology, or domain-specific modeling. Certificates from universities or recognized organizations can demonstrate commitment to skill development.

Professional Associations and Training: Joining groups such as the American Statistical Association (ASA) provides access to conferences, workshops, journals, and professional networks. These resources help statisticians learn from experts and stay up to date on industry trends.

Workplace Training and Mentorship: On-the-job training, mentorship from senior statisticians, and participation in cross-functional projects all contribute to professional growth. These experiences strengthen technical judgement and communication skills, both of which are essential for advancement.

Specialization in High-Demand Areas: Many statisticians deepen their expertise in areas like biostatistics, econometrics, machine learning, causal inference, or quality engineering. Specialization increases job opportunities and often leads to higher earning potential.

Industries Employing Statisticians

Since almost every sector uses data to guide decisions, assess risks, and improve performance, statisticians are everywhere. The roles will change somewhat by field, but the core principles remain the same. Here's where you'd find statisticians.

Healthcare and Pharmaceuticals: Many statisticians work in hospitals, public health organizations, or pharmaceutical companies. They support clinical trials, evaluate treatment effectiveness, analyze patient outcomes, and help regulatory agencies make evidence-based decisions about new drugs and medical interventions.

Government and Public Sector: Government statisticians work in national agencies, local authorities, and research bodies. They develop official datasets, run large-scale surveys, model economic or labour trends, and inform public policy on topics such as healthcare, education, housing, agriculture, and national security.

Finance and Insurance: Financial institutions depend heavily on statisticians for risk modeling, forecasting, investment analysis, and credit evaluation. Insurance companies use statisticians to assess probability, develop pricing models, and evaluate long-term risk.

Technology and Data-Driven Industries: Statisticians in tech analyze large datasets, design experiments, build predictive algorithms, and work closely with data engineers and product teams. This sector often blends classical statistical methods with machine learning and computational tools.

Manufacturing and Engineering: Industrial and quality statisticians monitor production lines, test product reliability, evaluate system performance, and support continuous improvement initiatives. Their work ensures safe, consistent, and efficient manufacturing processes.

Research and Academia: Universities, laboratories, and research institutes rely on statisticians to help design experiments, analyze scientific data, publish findings, and develop new statistical methods. Many statisticians in academia focus on advancing theory or supporting interdisciplinary research.

Market Research and Business Analytics: Statisticians in commercial sectors help organisations understand consumer behaviour, test marketing strategies, evaluate product performance, and guide business decisions using data-driven insights.

Nodes representing algorithms. — Algorithms are central to certain statistician roles. | Photo by Alina Grubnyak

Career Outlook and Salary Expectations

Statisticians have an excellent career outlook and expected salary as more and more industries leverage data and advanced analytics to make decisions and plan. Organizations are seeking professionals to interpret complex datasets to inform decision-making. No matter the industry, statisticians are in demand.

The median annual salary is

$103,000

for statisticians in the United States.

Employment Growth Trends: According to the U.S. Bureau of Labor Statistics, employment for mathematicians and statisticians is projected to grow 8 percent from 2024 to 2034, which is categorized as faster than average. This growth is driven by increased use of statistical analysis in public health, social policy, pharmaceuticals, and digital technologies. As more organisations adopt data-driven strategies, the need for statisticians with strong technical and communication skills will only increase.

Industry Demand and Sector Outlook: Some industries are expanding faster than others. Healthcare and pharmaceuticals continue to need statisticians for clinical trials and regulatory submissions, while finance relies heavily on forecasting and risk analysis. Technology and software companies are now among the strongest employers, especially those building products that rely on user analytics, experimentation, and statistical modelling. Government agencies also maintain stable long-term demand, particularly in public health, economics, labour analysis, and national surveys.

Salary Expectations and Earning Potential: Statisticians typically earn well above the national average, with opportunities for higher earnings in finance, technology, and specialized scientific roles. Entry-level positions often start at the lower end of the range, but salaries rise quickly with experience, particularly when statisticians move into modeling-intensive roles or take on team leadership responsibilities. Advanced degrees, specialised training, and strong programming skills can significantly increase earning potential.

Future Skills and Specialisations Driving Growth: The career outlook is especially strong for statisticians who develop expertise in machine learning, data engineering, computational statistics, biostatistics, or survey methodology. As AI-related tools continue to evolve, statisticians who understand both classical methods and modern algorithms will be well-positioned to secure the most competitive positions. Demand is also expected to grow for statisticians who can contribute to ethical data use, data-quality governance, and transparent modelling practices.

Industry	Median annual wage (USD, May 2024)	Notes
Computer systems design and related services	136,340	Includes software and tech firms; strong demand for experimentation and product analytics.
Federal government	128,940	Includes agencies such as Census, BLS, NIH; stable roles with strong benefits.
Research & development (physical, engineering, life sciences)	125,710	Covers private and nonprofit R&D labs; methodology development and scientific data analysis.
Healthcare and social assistance	99,540	Hospitals, public health, healthcare systems; biostatistics and outcomes research.
Colleges, universities, and professional schools (state)	81,920	Academic roles; research support and teaching responsibilities.