Building reusable analysis & visualization pipelines for behavioural research - BC-993

Project type: Innovation
Desired discipline(s): Computer science, Mathematical Sciences, Statistics / Actuarial sciences, Psychology, Social Sciences & Humanities
Company: Cloud Army Network Inc.
Project Length: 4 to 6 months
Preferred start date: 04/01/2026
Language requirement: English
Location(s): BC, Canada
No. of positions: 1
Desired education level: CollegeUndergraduate/BachelorMaster'sPhDPostdoctoral fellowRecent graduate
Open to applicants registered at an institution outside of Canada: No

About the company: 

CloudArmy is a research technology company specializing in online behavioural research, primarily for commercial and market research applications. We design and analyze experimental studies for clients using proprietary software, generating large volumes of structured data across a wide range of project types, with a particular focus on reaction time–based measures. Our work relies on statistical analysis to translate experimental data into clear, actionable insights.

CloudArmy is Canada-based, operating as a fully remote organization with team members distributed across Canada and internationally. We emphasize methodological rigor, transparency, and creative problem solving.

Describe the project.: 

CloudArmy conducts large-scale online behavioural research that generates complex experimental datasets, including extensive reaction time data. Although analyses are statistically rigorous, most of the work is currently performed through bespoke, project-specific workflows. As project volume increases, this limits scalability, consistency, and efficiency.

The objective of this project is to develop reusable analysis and visualization pipelines that convert one-off analytical workflows into standardized, parameterized systems that can be applied reliably across projetcs. This represents an innovation in CloudArmy’s internal processes, improving how analyses are structured, executed, scaled, and delivered to clients.

The candidate will review existing data structures, analytical workflows (including GUI-based statistical tools and scripts written in R and Python), and outputs. They will then work in a coding environment to design and implement reusable analysis and visualization pipelines. Key tasks include developing modular statistical analysis components informed by experimental design, embedding methodological decision-making into pipeline logic, and building end-to-end workflows that transform raw experimental data into consistent, polished, client-ready outputs.

Methodologically, the project will operationalize established statistical techniques (e.g., regression-based and mixed-effects models, post-hoc corrections) in a flexible, reproducible manner, while accounting for real-world data constraints.

By the end of the project, CloudArmy will have reusable analytical tools and standardized visualization outputs that improve efficiency, consistency, and scalability while preserving statistical rigor and transparency.

Required expertise/skills: 

Required skills:

1. Solid training in statistics and experimental design, with hands-on experience applying methods such as ANOVA, correlation, regression, and mixed-effects modeling.
2. Ability to reason through methodological choices, explain decisions clearly, and adapt analyses when real-world data do not meet ideal statistical conditions.
3. Experience working in a code-based analysis environment with proficiency in at least one statistical scripting language.
4. Familiarity with structuring analyses into reusable scripts or functions, rather than purely one-off analyses.
5. Experience contributing to end-to-end analytical workflows, including data preparation, analysis, visualization, and reporting.
6. Ability to produce clear, well-organized visualizations and analytical outputs suitable for client review.
7. Strong analytical thinking, attention to detail, and ability to work independently with guidance.

Assets:

1. Proficiency in R and/or Python
2. Familiarity with analysing real-world human behavioural data (familiarity with reaction time data in particular is a clear asset).