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$33K

$85.5K

$140.5K

How much do statistical scientist jobs pay per year?

As of Jul 10, 2026, the average yearly pay for statistical scientist in the United States is $85,539.00, according to ZipRecruiter salary data. Most workers in this role earn between $59,500.00 and $111,500.00 per year, depending on experience, location, and employer.

Is 40 too late for data science?

As a statistical scientist or data scientist, starting a career at 40 is feasible, as the field values skills, experience, and continuous learning. Many professionals transition into data science later in life by acquiring relevant skills such as programming, statistics, and machine learning through courses or certifications. Age is less important than your ability to adapt and develop expertise in data analysis tools like Python or R.

How does a Statistical Scientist typically collaborate with cross-functional teams in a research or corporate environment?

Statistical Scientists regularly work alongside colleagues from diverse backgrounds such as data engineering, software development, product management, and subject matter experts. Their role often involves translating complex statistical analyses into actionable insights for non-technical stakeholders, ensuring that data-driven recommendations are clearly understood and implemented. Effective communication and teamwork are crucial, as Statistical Scientists may participate in interdisciplinary meetings, provide guidance on experimental design, and contribute to strategic decision-making based on statistical evidence.

What are the key skills and qualifications needed to thrive as a Statistical Scientist, and why are they important?

To thrive as a Statistical Scientist, you need advanced knowledge of statistics, data analysis, and mathematical modeling, often supported by a degree in statistics, mathematics, or a related field. Proficiency in statistical software such as R, SAS, or Python, and familiarity with data visualization platforms are typically required. Strong problem-solving abilities, critical thinking, and effective communication skills help distinguish top performers in this role. These competencies are crucial for accurately analyzing complex data, conveying findings clearly, and supporting informed decision-making in research or business environments.

Is AI replacing statisticians?

AI is transforming the role of statistical scientists by automating data analysis tasks and enabling more complex modeling, but it does not replace the need for human expertise in designing studies, interpreting results, and making strategic decisions. Statisticians are increasingly using AI tools and programming languages like R and Python to enhance their work. The profession continues to evolve, emphasizing skills in data management, statistical theory, and machine learning.

What is the difference between Statistical Scientist vs Data Analyst?

AspectStatistical ScientistData Analyst
Required CredentialsMaster's or PhD in Statistics, Mathematics, or related fieldBachelor's or Master's in Statistics, Data Science, or related field
Work EnvironmentResearch-focused, often in R&D or academia, but also in industryBusiness or operational settings, focusing on data reporting and visualization
Employer & Industry UsageTech, healthcare, finance, research institutionsRetail, marketing, finance, healthcare

While both roles analyze data, Statistical Scientists focus on developing new statistical methods and models, often working on complex research projects. Data Analysts primarily interpret existing data to generate reports and insights for business decisions. The roles overlap in skills and tools but differ in scope and depth of statistical expertise.

What does a statistical scientist do?

A statistical scientist analyzes data to identify patterns, develop models, and inform decision-making across various fields. They use statistical software and programming languages like R or Python and often collaborate with other teams to interpret results and support research or business strategies.

What are Statistical Scientists?

Statistical Scientists are professionals who specialize in using statistical methods to collect, analyze, and interpret data. They design experiments, develop data models, and apply mathematical theories to solve real-world problems across various industries, including healthcare, finance, technology, and government. Their work helps organizations make informed decisions, identify trends, and develop predictive insights. Statistical Scientists often work closely with data scientists, researchers, and subject matter experts to ensure that data-driven conclusions are accurate and actionable.

Are statisticians highly paid?

Statisticians, including statistical scientists, tend to have competitive salaries due to their specialized skills in data analysis, modeling, and programming. Salaries vary by industry, experience, and location, but they are generally considered well-paid compared to many other professions in the sciences and analytics fields.
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Statistical Scientist jobs near you

Data Scientist with Security Clearance

SE2S

Charlottesville, VA • On-site

Other

Re-posted 27 days ago

Job description

SE2S is seeking a Mathematical/Statistical Scientist who possesses a strong background in the development and application of data mining, analysis, aggregation, fusion, and visualization in order to process, analyze, and integrate raw data facilitating the production and dissemination of scientific and technical intelligence and general military intelligence relating to foreign ground forces research, development, systems, and operations. Focus of work is to contribute to data- driven all-source analysis, assessment, and decision making and implement innovative methodologies which enhance production and dissemination activities.
Job Responsibilities:
• Processing, tagging/labeling/parsing/indexing unstructured, semi-structured, and structured classified and unclassified data sets.
• Developing research designs (including formulating testable questions, identifying and defining key concepts and variables and matching appropriate methods to problems.)
• Data project management (including cleaning and preparing data) and creating, structuring, and maintaining data sets.
• Data analysis (including manipulating datasets and interpreting findings using a mix of programs/environments (such as R, Python, Matlab) and techniques (e.g., text and pattern analysis, supervised and unsupervised data clustering, classification, change/anomaly detection, time-series, regression, geospatial, and/or social network analysis.)
• Developing process flows/visualizations and technological developmental roadmaps and capturing them in the NGIC TechWatch Tool.
• Using text/web data analytics for establishing and expanding keyword searches and using bibliometric analysis tools to perform horizon scans and deep dives into NGIC identified S&T research fields.
• Creating mathematical models and coding in R, Python, MatLab, and Excel
• Natural Language Processing.
• Providing knowledge/familiarity of technical language.
• Providing experience with Big Data (parallel processing power and options for handling analysis of data)
• Understanding the retrieval processes from databases using industry-standard query languages (e.g., SQL).

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