I am a research software engineer who helps scientific teams turn exploratory analysis into reproducible systems, reusable code, and stronger engineering habits.
Today I work at Harvard University while completing graduate study in Health Informatics at Northeastern University. Across academia and industry, I have worked in neuroimaging, psychology, human-computer interaction, data science, and climate-focused research.
Shoutout to PIs who give a crap… I don’t buy the whole “your company is your family” thing but treating your team like they are valuable and involving them in the good times and the bad can go a long way #academia #acadamicsky
— TinasheMichaelTapera (@tinashemtapera.bsky.social) 2025-05-20T21:35:49.274Z
What I Bring to a Team
- R and Python workflow design
- Notebook-driven development and literate programming
- Testing, CI/CD, and environment management
- Clear documentation, onboarding, and technical training
What I Care About
- Open and FAIR science
- Reproducible computation
- Accessible programming practice
- Research software that survives handoff and reuse
Career Arc
I grew up in Zimbabwe, where writing, music, and performance were my first serious creative outlets. That background still shapes how I think about technical work: good software should communicate clearly, tell the truth about how it was made, and remain usable by more than the person who built it.
I moved to the United States to study psychology at Drexel University and later completed an accelerated BS/MS track in the field. That training pulled me toward quantitative methods, machine learning, and programming in R and Python, and it gave me my first real experience building analyses that had to stand up inside collaborative research.
Since then, I have worked across academia and industry: as a data science intern at Salesforce, as a neuroimaging data analyst at the Penn Lifespan Informatics and Neuroimaging Center, and now as a research software engineer supporting research at Harvard. Across those settings, the throughline has been consistent. I am at my best when a team needs someone who can bridge scientific context, analytical code, and software engineering discipline.
What I Like Building
- Reproducible pipelines for data transformation, analysis, and reporting
- Notebook-native workflows that evolve cleanly into packages, apps, or production services
- Documentation, templates, and internal tooling that make a lab or team more self-sufficient
- Training and mentorship that help young scientists become effective programmers in R and Python
Where I Am Headed
My long-term goal is to become a leading expert in research software engineering, with particular depth in notebook-driven development and reproducibility. I am especially interested in work that improves team practice, research throughput, and the long-term maintainability of open scientific projects.
Outside Work
- Member of, and Lead Newsletter Editor for, the US Research Software Engineer Association (Read the newsletter here)
- Mentor in the Data Science Learning Community (DSLC)
- I jump rope is my preferred exercise — seriously, it’s really fun and a great workout!
- Music, writing, and storytelling still influence how I teach and build software