I have the opposite question from other people in the thread - this seems pretty open-ended.
Just as an example, I do bioinformatics work, and "technical system" for someone I'm interviewing (as I understand your question) might encompass any of:
- a workflow manager: how it works under the hood, common patterns for modularity/reuse or configuration
- a specific pipeline: the stages of cleaning and quantifying raw data, the rationale for various stages and the tools chosen to execute them (including how to benchmark various methods against each other)
- a specific third-party tool: theoretical details of the algorithm, practical considerations for when to apply one over another
- familiarity with common general-purpose packages or APIs (e.g. pandas, numpy, scikit-learn)
- QC: common metrics for QC'ing various experimental data, how to decide if data is "good enough", how to troubleshoot biological vs technical (lab) vs technical (computational) sources of error
- biology: technical details of an experiment, the technologies generating the data, or the underlying biology
- data analysis: how to choose and make relevant figures, any of many data-science/ML topics (e.g. clustering), connecting data to relevant domain questions
- devops: data storage/management on HPC or cloud, HPC job schedulers, any of many cloud topics (e.g. IAC, setting up a database or cloud execution of a pipeline)
I'm more curious about how you guide a candidate towards selecting a system that gives you the most relevant perspective on their thinking and skillset - do you provide any suggestions, or are there typically pretty evident choices based on the role?
Just as an example, I do bioinformatics work, and "technical system" for someone I'm interviewing (as I understand your question) might encompass any of:
- a workflow manager: how it works under the hood, common patterns for modularity/reuse or configuration
- a specific pipeline: the stages of cleaning and quantifying raw data, the rationale for various stages and the tools chosen to execute them (including how to benchmark various methods against each other)
- a specific third-party tool: theoretical details of the algorithm, practical considerations for when to apply one over another
- familiarity with common general-purpose packages or APIs (e.g. pandas, numpy, scikit-learn)
- QC: common metrics for QC'ing various experimental data, how to decide if data is "good enough", how to troubleshoot biological vs technical (lab) vs technical (computational) sources of error
- biology: technical details of an experiment, the technologies generating the data, or the underlying biology
- data analysis: how to choose and make relevant figures, any of many data-science/ML topics (e.g. clustering), connecting data to relevant domain questions
- devops: data storage/management on HPC or cloud, HPC job schedulers, any of many cloud topics (e.g. IAC, setting up a database or cloud execution of a pipeline)
I'm more curious about how you guide a candidate towards selecting a system that gives you the most relevant perspective on their thinking and skillset - do you provide any suggestions, or are there typically pretty evident choices based on the role?