R Engineer CV: Example and Template
How to structure your CV as an R Engineer for the Swiss job market - with validated packages, reproducible pipelines and measurable results.
CV example — two-page template
This template is editable straight away — or upload your existing CV and it is carried into this layout automatically.
What qualification is expected for an R Engineer in Switzerland?
A BSc or MSc in statistics, biostatistics, data science, mathematics or computer science is standard - but what matters most is proof that you can operate R in production.
Typical paths include an MSc in Biostatistics (UZH, Basel), an MSc in Statistics (ETH Zurich, University of Neuchâtel) or a BSc/MSc in Data Science at a university of applied sciences such as ZHAW, FHNW or HSLU. State the degree with title, field, institution and year; for master's degrees, also mention the thesis topic if it is methodologically relevant (for example mixed models, Bayesian inference or survival analysis). For foreign diplomas, add a note on recognition by Swissuniversities, if available.
For career changers from physics, econometrics, epidemiology or actuarial science, what counts most in Switzerland is the portfolio: public R packages, CRAN releases, contributions to tidyverse or pharmaverse projects, and a GitHub or GitLab profile with readable package code. Complementary qualifications such as a CAS in Data Engineering, the SAA actuarial diploma or Posit certifications are more credible than a plain list of online courses. Avoid listing ten MOOCs with no project connection.
It is important to distinguish yourself from a Data Scientist: as an R Engineer, you are responsible for package structure, versioning, deployment and operations. Make this visible in the education and certificates section by explicitly naming topics such as R CMD check, renv lockfiles, Posit Connect, Docker images or GAMP 5 validation. This lets HR immediately recognise that you don't just write analyses - you ship software.
How do I present professional experience as an R Engineer convincingly?
For each position, describe the context, the R tools used, and three to four results with figures on runtime, users, test coverage or costs.
Start each role with a context sentence: industry, team size, data volume and level of regulation. A sentence like 'R platform team for clinical reporting, 6 people, GxP-regulated' says more than three lines of task description. Follow with achievements in the form action - tool - impact, for example 'Introduced targets pipeline with data.table and Arrow, reducing runtime from 9h to 22min'. Avoid phrases like 'responsible for analyses'.
Quantify what is actually measured in R projects: number of production packages, test coverage in percent, runtime reduction, number of Shiny users, load times, number of automated reports, saved person-days, or infrastructure costs in CHF (for example CHF 41'000 per year). If you work in pharma, mention study numbers, CDISC standards and passed inspections; in insurance, policy volumes, simulation numbers and FINMA-relevant model governance.
The Swiss CV is built in reverse chronological order, without gaps in the month details. Add a compact technology section with a clear separation between languages (R, C++ via Rcpp, SQL, Python), frameworks (tidyverse, data.table, Shiny, targets, plumber) and platforms (Posit Workbench/Connect, Docker, Kubernetes). List freelance assignments and open-source contributions as separate positions with client type and scope in person-days.
How long should the CV be, and does it need a photo?
Two pages is the benchmark; a professional photo is still customary in Switzerland and expected by most employers.
Keep it to two pages: profile, at a glance, professional experience from the last ten to twelve years, technologies, education, certificates and languages. Summarise older positions in a single line. A code portfolio does not belong in the CV itself, but as a link to GitHub, GitLab or a pkgdown site in the header - make sure the linked repositories actually pass R CMD check, because technical interviewers will look exactly there.
A neutral portrait photo in the top right, nationality or residence permit (B, C, G) and place of residence are standard in Switzerland. Roles in pharma or banking often require a security clearance or a criminal record extract; a note on availability and notice period (three months is common) helps with planning. Only state salary expectations if explicitly requested - realistic ranges lie roughly between CHF 100'000 and CHF 160'000, depending on experience and region.
Weight languages according to region: in Basel and Zurich, English dominates as the working language; in Geneva, Lausanne and Neuchâtel, French is often a requirement; federal offices and cantons value German and French. State levels according to the CEFR (C1, B2) rather than 'good'. Submit the CV as a PDF with a meaningful file name, and tailor the profile and technology section specifically to the job posting, for example towards Shiny development, pharmaverse or platform operations.
The CV in full
To read through and reuse.
Nadine Brechbühl
R Engineer / Senior Statistical Software Engineer, MSc Biostatistics UZH
R Engineer with 8 years of experience building production R infrastructure for pharma, insurance and public statistics. I develop validated R packages, reproducible targets pipelines and Shiny applications operated by business departments without IT support. Focus on Posit Connect/Workbench, package governance with renv and rspm, as well as GxP- and FINMA-compliant documentation of analysis code.
What sets me apart
R packages instead of script collections: I package analysis code as tested R packages using testthat, roxygen2 and pkgdown - test coverage consistently above 85 percent, R CMD check without notes in CI.
Reproducibility as standard: renv lockfiles, Posit Package Manager snapshots and targets pipelines made 100 percent of regulatory analyses bit-exact reproducible, even after R minor upgrades.
Validation in regulated environments: Experience with GxP validation of R (Installation Qualification, risk classification per GAMP 5) and with model governance requirements under FINMA circulars.
Bridge between statistics and engineering: MSc in Biostatistics plus Rcpp, Arrow and Kubernetes skills: I optimise model code by a factor of 20 and take it into deployment myself.
Key achievements
Reporting runtime reduced from 9 hours to 22 minutes. Migrated a monolithic 12'000-line script into a targets pipeline using data.table and Arrow parquet; computing costs fell by CHF 41'000 per year.
Validated submission environment established. Introduced an R validation framework for 3 study teams (pharmaverse, admiral); reduced release time for ADaM datasets from 15 to 4 working days, passed audit without findings.
Shiny platform with 900 active users. Modularised Shiny app using golem, bslib and an async backend; load time reduced from 14 to 1.8 seconds, support tickets down 63 percent.
Experience
Senior R Engineer — Helvetia Biometrics AG, Basel, 03/2021 - present
R platform team for clinical reporting in a GxP-regulated environment, 6 people, working with biostatistics and QA.
- Developed 9 internal R packages (admiral extensions, TLG generators) and delivered them to 120 statisticians via Posit Package Manager
- Reduced nightly pipeline runtime from 9h to 22min, cutting cloud costs by CHF 41'000 per year
- Introduced R validation process per GAMP 5; 2 regulatory inspections without findings on analysis code
- Established GitLab CI pipeline with R CMD check on 3 R versions, increasing test coverage from 41 to 88 percent
R Developer / Data Engineer — Aare Assekuranz Analytics, Bern, 08/2018 - 02/2021
Non-life actuarial work, pricing and reserving with R; interface to Oracle DWH and FINMA reporting.
- Put pricing model (GLM/GAM with mgcv) into production as an R package, valuing 1.2 million policies per night
- Built Shiny dashboard for claims reserves for 180 users, reducing manual Excel work by 22 hours per week
- Rcpp implementation of chain-ladder bootstrap: 1'000 simulations reduced from 48min to 2.5min
- Introduced renv and Docker images for 14 analysis projects, shortening onboarding of new actuaries from 3 weeks to 4 days
Statistical Programmer R — Limmat Data Institute, Zurich, 09/2016 - 07/2018
Contract research for cantons and federal offices, official statistics and survey analysis.
- Parameterised and automated 24 R Markdown reports, shortening publication cycle from 6 to 2 weeks
- Implemented weighting and extrapolation routines (survey package) for 5 surveys with over 8'000 cases each
- Designed internal R training for 35 employees and delivered it 6 times
- Built data quality checks with pointblank, catching 340 inconsistencies before publication
Education
MSc, Biostatistics — University of Zurich · 2016
BSc, Statistics and Mathematics — University of Bern · 2014
CAS, Data Engineering — ZHAW School of Engineering · 2020
Posit Certified Trainer - Tidyverse (2022) · Posit Connect Administration (2021) · GAMP 5 / Computerised System Validation, SGS (2022) · Certified Kubernetes Application Developer, CKAD (2023)