NGS Scientist I
Posted Sep 15, 2026 · We last checked this listing on Oct 6, 2026
Likely interview questions for this role
Written from this job description, not a generic list. Each one notes what the interviewer is really checking.
About your past
Tell me about an NGS library prep or molecular assay you helped develop or optimize. What did you change and how did you measure the result?
What they are checking: Whether they have real hands-on assay development experience and judge success with data.
Tell me about a time an experiment didn't give you the answer you hoped for. What did you do with it?
What they are checking: Whether they report results honestly and learn from them rather than rerunning until they look good.
What would you do
Your library yields drop suddenly across several runs. How do you troubleshoot it?
What they are checking: Whether they isolate variables systematically instead of changing several things at once.
How does working in a high-throughput clinical lab change how you design a process compared with a research lab?
What they are checking: Whether they think about scalability, automation and robustness for daily clinical use.
You think a step in a validated workflow could be improved, but the lab is busy running clinical samples. How do you propose the change?
What they are checking: Whether they respect change control in a regulated lab while still pushing improvements.
About the work
How would you design a validation study for a new reagent lot in a CAP/CLIA/NYSDOH lab?
What they are checking: Whether they know acceptance criteria, sample selection and documentation that regulators expect.
What's the difference between accuracy, precision, sensitivity and limit of detection in assay validation, and how would you measure each?
What they are checking: Whether they understand the core performance characteristics a clinical assay must prove.
How do you design an R&D experiment so the results are actually trustworthy?
What they are checking: Whether they think about controls, replicates and confounders before running anything.
How do you keep your lab notebook and documentation good enough to support a regulatory review?
What they are checking: Whether their records would let someone else reproduce and audit their work.
What sequencing QC metrics do you look at first when judging whether a run is good?
What they are checking: Whether they know practical run metrics like coverage, duplication and quality scores.
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As published by Tempus AI.