Statistical Computing Platform Engineer

Statistical Computing Platform Engineer
Building collaborative environments for data science and statistical programming

Position Overview
We are seeking a Statistical Computing Platform Engineer to design, deploy, and manage shared environments for collaborative statistical analysis and algorithm development. This role will focus on platforms like JupyterHub and GitLab, enabling statisticians, economists, and data scientists to collaboratively write, run, version, and share statistical code using open standards and reproducible workflows.

The ideal candidate will have a background in data science infrastructure, DevOps for analytical environments, and a strong interest in enabling transparent, scalable statistical collaboration.

Key Responsibilities

Platform Design & Deployment

• Design and deploy secure, scalable environments for collaborative statistical work using JupyterHub, RStudio Server, and similar notebook-based tools
• Integrate version control (e.g. GitLab, GitHub) and CI/CD pipelines into statistical workflows for peer review and reproducibility
• Implement multi-user compute environments with isolated kernels, persistent storage, and resource quotas

Infrastructure & Automation

• Automate the provisioning of shared notebooks, computational backends, and environments using Docker, Kubernetes, or Terraform
• Maintain environments with pre-configured libraries for Python, R, and Stata, optimized for statistical work
• Implement monitoring, logging, and performance tracking for usage and troubleshooting

Collaboration Enablement

• Support integration of shared development workflows, code repositories, and notebook-sharing templates
• Enable real-time and asynchronous collaboration on models, scripts, and results across distributed teams
• Develop templates and best practices for reproducible analysis pipelines and peer-reviewed code

Security & Compliance

• Manage user access, authentication (OAuth, LDAP, SSO), and secure execution of notebooks in shared environments
• Ensure compliance with data protection policies and sandboxing of user workloads

Required Qualifications

Technical Skills

• 6+ years experience managing shared environments for data science or statistical analysis (e.g. JupyterHub, RStudio Server, VSCode Server)
• Proficiency with DevOps practices and tools (Docker, Kubernetes, GitLab CI/CD, Ansible, Terraform)
• Experience supporting statistical programming languages (Python, R, Stata) in a production environment
• Knowledge of version control, collaborative code workflows, and reproducible research practices

Soft Skills

• Ability to work closely with statisticians, researchers, and data scientists to translate workflow needs into platform features
• Strong communication and documentation skills
• Passion for open science, transparency, and collaboration

Preferred Qualifications

• Bachelors or Masters degree in Computer Science, Statistics, Data Science, or a related technical field
• Experience in academic, governmental, or international research organizations
• Familiarity with HPC environments or cloud-based statistical computing (e.g., GCP, AWS, Azure for research)
• Background in open data workflows, FAIR principles, or statistical methodology