Occupation intelligence

statistical assistant

Key facts

Interested in a career where you analyze data and contribute to informed decision-making? As a statistical assistant, you’ll play a vital role in collecting, organizing, and interpreting data to support research and reporting.

Summary

Statistical assistants are essential members of teams conducting statistical studies. Your day-to-day work involves gathering data from various sources, applying statistical formulas, and creating clear, visual representations of findings. You’ll be responsible for ensuring data accuracy and contributing to the creation of comprehensive reports that inform strategic decisions. This role requires attention to detail, analytical skills, and the ability to communicate complex information effectively.

Key responsibilities
  • • Collecting and organizing data from diverse sources, ensuring accuracy and completeness.
  • • Applying statistical formulas and techniques to analyze data sets.
  • • Creating charts, graphs, and other visual aids to present data findings clearly.
15%
Resilience Score · 2026 (Higher is better)
Short-cycle tertiary education 81% AI exposure
Start Career DNA assessment
Labour market

Where this occupation is in demand

Reported labour shortages and surpluses, by year. Published for occupation groups, not for individual job titles.

Shortage reportedSurplus reportedReported in another yearNot covered by this source

Deeper colour: reported the same way in more consecutive years.

Figures cover Business and administration associate professionals — 200 jobs including this one.

3 of 8 in shortage202521 of 32 growing4.2Mopenings to 2035

In shortage: Germany, Italy, Netherlands.

Longest-running shortage: Netherlands, 4 years.

Select a place on the map to see its figures.

About this source

Source: ELA/EURES labour shortages and surpluses. Readings are published at occupation-group level, and cover Europe. Editions differ in annex layout and country coverage, so a change between years does not always mean the labour market changed. Countries in grey were not reported, which is not the same as being in balance.

Explore More

Find your career path and explore the science behind our recommendations.

Guiding others? See NexPath for schools and practices.
Quick fit check

Could statistical assistant fit you?

Answer three quick questions. This is not a full assessment — it is a teaser to help you decide whether to compare your profile.

Progress0/3

Do you enjoy tasks that require Analytical Thinking?

Do you enjoy tasks that require Integrity?

Do you enjoy tasks that require Attention to Detail?

NexFuture™

Future Outlook for statistical assistant

The outlook for statistical assistant reflects a balanced mix of automation exposure and durable, human-led work.

How are these scores calculated?

The Resilience Score (0–100) estimates how structurally protected this occupation is from automation and AI disruption, based on task-level analysis. Higher scores mean more human-judgment-intensive tasks. AI Exposure shows the estimated percentage of task hours that current AI capabilities could affect. These are model-derived structural indicators, not predictions about individual job security.

Play the future

How could statistical assistant change as AI adoption grows?

Several task areas may shift toward AI-assisted workflows, so reskilling becomes more important.

Significant task-level transformation is estimated in 8 years (around 2034) under the selected Expected Pace scenario.
~10%
Resilience
Automation Risk
EXP~85%
Human advantage
MOAT~10%

Illustrative scenario based on task automatability — not a forecast. Values are rounded the further ahead you look.

2026
2030
2039
AI Adoption Speed:

How AI may change this role

Deterministic, model-based interpretation of current role signals — not a guarantee of replacement.

Human-owned 15% Human-owned
What still depends on people

Most tasks here are AI-assistable rather than purely human-led.

The Human Edge To stay ahead in this role, focus on mathematics and quantitative analysis. These human-centric skills are the hardest for AI to replicate in the next 20 years.
Assist 37% Assist
Where AI may become a co-pilot
  • identify statistical patterns
  • apply statistical analysis techniques
  • apply scientific methods
Automate 81% Automate
Tasks most exposed to automation
  • gather data
  • process data
  • execute analytical mathematical calculations
Detailed Analysis

Vital Signs & AI Vectors

AI Exposure Vectors

0-100%
AI / Machine Learning 37%

Exposure to AI-assisted analysis, pattern recognition, and predictive modelling tasks

Generative AI 6%

Exposure to content generation, creative augmentation, and large language model tools

Cognitive Software 2%

Exposure to workflow automation, decision-support software, and process digitisation

Robotic & Physical Automation 0%

Exposure to physical automation, robotics, and sensor-driven task displacement

Technical Details
Methodology: NexFuture v3.0 Sources: O*NET® 30.3, ESCO v1.2.1 Updated: Aug 2026

NexFuture v3.0 estimates automation exposure natively from ESCO essential-skill groups, weighted by skill mass and calibrated against expert anchors. Scores are probabilistic estimates, not guarantees. See the NexFuture Methodology White Paper for full details.

Measures automation exposure. It does not measure pay, demand, or how many jobs exist near you.

Day in the life

What people in this role usually do

Digital Technology

Day in the life

A typical day as a statistical assistant

09
09:00 · Morning
apply scientific methods
Apply scientific methods and techniques to investigate phenomena, by acquiring new knowledge or correcting and integrating previous knowledge.
10
10:30 · Mid-morning
apply statistical analysis techniques
Use models (descriptive or inferential statistics) and techniques (data mining or machine learning) for statistical analysis and ICT tools to analyse data, uncover correlations and forecast trends.
12
12:00 · Midday
conduct quantitative research
Execute a systematic empirical investigation of observable phenomena via statistical, mathematical or computational techniques.
14
14:00 · Afternoon
execute analytical mathematical calculations
Apply mathematical methods and make use of calculation technologies in order to perform analyses and devise solutions to specific problems.
15
15:30 · Late afternoon
gather data
Extract exportable data from multiple sources.
17
17:00 · Wrap-up
identify statistical patterns
Analyse statistical data in order to find patterns and trends in the data or between variables.

Task order is illustrative. Individual days vary.

Software & Technologies & Knowledge areas
Software & Technologies
A programming language APLAvidian Technologies ProphetBenfield ReMetricaBentley MicroStationC#C++Corel WordPerfect Office SuiteDatabase softwareData visualization softwaredBASEGGY AXISGoogle Workspace softwareHarvard GraphicsHyland OnBase Enterprise Content ManagementIBM Lotus 1-2-3IBM SPSS StatisticsInsightful S-PLUSMicrosoft AccessMicrosoft Active Server Pages ASPMicrosoft Excel
Knowledge areas
  • data quality assessment

    The process of revealing data issues using ​quality indicators, measures and metrics in order to plan data cleansing and data enrichment strategies according to data quality criteria.

  • statistical modeling techniques

    The approaches for employing statistical analysis to dataset within the data science field. It seeks to elaborate reality predictions through statistical models and explicit assumptions.

Cross-sector skills
  • mathematics
  • quantitative analysis
  • statistical analysis system software
Essential skills
conducting academic or market research
  • apply scientific methods

    Apply scientific methods and techniques to investigate phenomena, by acquiring new knowledge or correcting and integrating previous knowledge.

  • conduct quantitative research

    Execute a systematic empirical investigation of observable phenomena via statistical, mathematical or computational techniques.

technical or academic writing
  • write work-related reports

    Compose work-related reports that support effective relationship management and a high standard of documentation and record keeping. Write and present results and conclusions in a clear and intelligible way so they are comprehensible to a non-expert audience.

  • write technical reports

    Compose technical customer reports understandable for people without technical background.

analysing scientific and medical data
  • identify statistical patterns

    Analyse statistical data in order to find patterns and trends in the data or between variables.

gathering information from physical or electronic sources
  • gather data

    Extract exportable data from multiple sources.

managing, gathering and storing digital data
  • perform data analysis

    Collect data and statistics to test and evaluate in order to generate assertions and pattern predictions, with the aim of discovering useful information in a decision-making process.

performing calculations
  • execute analytical mathematical calculations

    Apply mathematical methods and make use of calculation technologies in order to perform analyses and devise solutions to specific problems.

analysing and evaluating information and data
  • apply statistical analysis techniques

    Use models (descriptive or inferential statistics) and techniques (data mining or machine learning) for statistical analysis and ICT tools to analyse data, uncover correlations and forecast trends.

entering and transforming information
  • process data

    Enter information into a data storage and data retrieval system via processes such as scanning, manual keying or electronic data transfer in order to process large amounts of data.

Skill DNA

Skill DNA

Work personality traits and values that define this role

Key traits you need
Analytical Thinking Integrity Attention to Detail Dependability Cooperation Initiative Achievement/Effort Persistence Adaptability/Flexibility Stress Tolerance Self-Control Independence Innovation Leadership Concern for Others Social Orientation
Key rewards you can expect
AchievementWorking Condit…RecognitionRelationshipsSupportIndependence
Career progression

Growth Pathways & Similar Roles

Explore typical career progression paths, adjacent skills, and similar roles to plan your next transition.

Common questions

Frequently asked questions

What kind of educational background is typically needed to become a statistical assistant?
While a formal degree isn't always required, a background in mathematics, statistics, or a related field is highly beneficial. Many statistical assistants hold an associate's or bachelor's degree, or have completed relevant coursework.
Are there specific software programs I should learn to be a successful statistical assistant?
Familiarity with statistical software packages like Microsoft Excel, SPSS, or R is generally expected. Proficiency in data visualization tools is also a valuable asset.
What career progression opportunities are available for statistical assistants?
With experience and further education, statistical assistants can advance to roles such as statistical analyst, data scientist, or research statistician. Leadership and strategy skills (as indicated by your career band) can lead to supervisory roles within statistical teams.
Statistical Assistant — is there a shortage in Europe?
No. In the 2025 ELA/EURES edition, a surplus was reported in 5 of the 8 European countries that assessed this occupation group: Greece, Finland, Latvia, Sweden and 1 more. 3 countries reported a shortage. These assessments are published per occupation group rather than per job title.
Statistical Assistant — what does it pay in the United States?
$51,440 a year at the median, as of 2025-05. State medians run from $27,490 to $82,580. Source: US Bureau of Labor Statistics. This is a United States figure and not a projection for Europe.