About ProCogia:

ProCogia is a data and AI consulting firm helping organizations turn complex technology challenges into measurable business outcomes. We work across highly regulated and high-stakes industries, including telecommunications, financial services, life sciences, healthcare, and the public sector, where security, governance, reliability, and performance are critical. Our teams bring together expertise across data science, engineering, AI, analytics, and consulting to design and deliver practical, production-ready solutions. We are actively advancing capabilities across generative AI, agentic systems, workflow automation, AI governance, and enterprise AI applications. At ProCogia, you will work alongside experienced practitioners on meaningful technical challenges and contribute directly to both client solutions and the platforms and products we are building internally.

Our Core Values: Trust, Growth, Innovation, Excellence, and Ownership

About ProCogia

We’re a diverse, close-knit team with a common pursuit of providing top-class, end-to-end data solutions for our clients. In return for your talent and technical expertise, you will be rewarded with a competitive salary, generous benefits along with ample opportunity for personal development. ‘Growth mindset’ is something we seek in all our new hires and has helped drive much of our recent growth across North America. Our distinct approach is to push the limits and value derived from data. Working within ProCogia’s thriving environment will allow you to unleash your full career potential. ProCogia has experienced considerable growth over the last three years & core to our culture is maintaining a high level of cultural equality throughout the company. Our diversity and differences allow us to create innovative and effective data solutions for our clients.

Location: Vancouver (On-site)

Job Summary

We are seeking a motivated and curious Statistical Programming Intern to join our team from July – December 2026. If the co-op is available in July, we would like them to start early and work for part of the summer as well as the defined co-op term from September-December. In this role, you will contribute to the development of internal workflows and tooling that help modernize analytical codebases across SAS, R, and Python, with a particular focus on using AI-assisted development tools to improve translation, testing, and workflow efficiency.

Key Responsibilities

Contribute to internal workflows and tools that help us translate and modernize analytical code between SAS, R, and Python.
Read and reason about existing SAS programs — understanding the business logic, statistical methods, data transformations, and workflow structure — and help translate them into modern equivalents.
Help design, test, and refine AI-assisted and agentic workflows for code translation, validation, and quality checking. You'll work alongside the team on internal projects first, with the chance to contribute to client-facing work as you ramp up.
Validate that translated code is correct — comparing outputs against the original, checking edge cases, cleaning logs, and documenting how you know the translation is faithful. This validation discipline is the heart of the work.
Work with senior consultants to document patterns, write up translation approaches, and contribute to knowledge-sharing within the team — through demos, internal walkthroughs, or written documentation.

What You Bring

Currently enrolled in a university program in third year or above in Applied Statistics, Data Science, Biostatistics, Bioinformatics, Statistics, Computer Science, Mathematics, or a closely related field.
Fluent in R for data work — idiomatic tidyverse to wrangle, join, reshape, and derive variables, with clear reasoning about the statistical operation behind each step.
Comfortable across R’s idioms – vectors, data frames / tibbles, factors, dates, and at least one class from S3/S4/R6. You produce analysis ready tables and graphics.
Hands on experience with interactive data analysis is plus such as Shiny and plotly