Statistical Computing Platform Engineer

il y a 6 heures

Paris, Île-de-France Blackfluo Temps plein

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 toolsIntegrate version control (e.g. GitLab, GitHub) and CI/CD pipelines into statistical workflows for peer review and reproducibilityImplement multi-user compute environments with isolated kernels, persistent storage, and resource quotasInfrastructure & Automation
Automate the provisioning of shared notebooks, computational backends, and environments using Docker, Kubernetes, or TerraformMaintain environments with pre-configured libraries for Python, R, and Stata, optimized for statistical workImplement monitoring, logging, and performance tracking for usage and troubleshootingCollaboration Enablement
Support integration of shared development workflows, code repositories, and notebook-sharing templatesEnable real-time and asynchronous collaboration on models, scripts, and results across distributed teamsDevelop templates and best practices for reproducible analysis pipelines and peer-reviewed codeSecurity & Compliance
Manage user access, authentication (OAuth, LDAP, SSO), and secure execution of notebooks in shared environmentsEnsure compliance with data protection policies and sandboxing of user workloadsRequired 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 environmentKnowledge of version control, collaborative code workflows, and reproducible research practicesSoft Skills
Ability to work closely with statisticians, researchers, and data scientists to translate workflow needs into platform featuresStrong communication and documentation skillsPassion for open science, transparency, and collaborationPreferred Qualifications
Bachelors or Masters degree in Computer Science, Statistics, Data Science, or a related technical fieldExperience in academic, governmental, or international research organizationsFamiliarity 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

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