Statistical Assistants

Compile and compute data according to statistical formulas for use in statistical studies. May perform actuarial computations and compile charts and graphs for use by actuaries. Includes actuarial clerks.

The occupation "Statistical Assistants" has an automation risk of 75.6%, which is closely aligned with its base risk of 76.8%. This relatively high exposure to automation is primarily because much of the work relies on computational and repetitive tasks that computers excel at. Advances in artificial intelligence and machine learning, combined with modern data processing software, have made it increasingly feasible to automate these core responsibilities. As technology continues to evolve, the reliance on human input for purely calculative or verification-based tasks diminishes, further elevating the automation risk for this profession. With a significant proportion of daily tasks lacking the need for deep human intuition or contextual judgment, "Statistical Assistants" find themselves particularly susceptible to technological displacement. Among the top three most automatable tasks are "Compute and analyze data, using statistical formulas and computers or calculators," "Check source data to verify completeness and accuracy," and "Enter data into computers for use in analyses or reports." These tasks are highly rule-based, depend heavily on routine inputs, and largely consist of operations that modern software can perform swiftly and accurately. For instance, computers are already adept at performing complex calculations and analyses without human error, and data entry has long been a target for automation due to its repetitive nature. Verifying data completeness and accuracy can be streamlined with algorithms designed to detect inconsistencies or missing information. This makes automation both attractive and practical in reducing costs and increasing efficiency. However, certain aspects of the statistical assistant's job remain more resistant to automation, primarily where human creativity or judgment is needed. The three most resistant tasks include "Send out surveys," "Discuss data presentation requirements with clients," and "Select statistical tests for analyzing data." These activities require interpersonal skills, the ability to interpret nuanced client requirements, and the application of expert knowledge in selecting appropriate methodologies. Moreover, bottleneck skills such as originality—with measured levels of just 3.0% and 3.1%—are minimally required in this field, suggesting that the job draws little upon qualities that current AI struggles to replicate. As a result, while automation will likely dominate the technical routine workload, a residual need for human flair and communication will persist, preventing complete automation of the occupation.