Data Strategy Lead - Hybrid
Posted Jul 8, 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 a study start-up that was at risk of missing its date. How did you recover it?
What they are checking: Whether they can spot timeline risk early, escalate clearly and lead a recovery, because start-up dates drive the entire clinical program behind them.
Tell me about a process you improved in clinical data management. What was the result?
What they are checking: Whether they have changed how work actually gets done and can show a measurable result, not just suggest improvements that never landed.
What would you do
The study team wants to add an eCOA and a new wearable device two months before first patient in. What do you do?
What they are checking: Whether they can assess integration, validation and timeline risk honestly and give the team clear options rather than just agreeing or refusing.
How do you explain a complex data system tradeoff to a clinical lead so they can make a decision?
What they are checking: Whether they can frame technical trade-offs in terms a clinical lead can decide on, since data decisions often sit with people outside data management.
How do you work with vendors who build the EDC so their build matches your specification?
What they are checking: Whether they write precise requirements and check vendor builds closely, because EDC errors found late are expensive and can compromise study data.
About the work
Walk me through the data management start-up deliverables from final protocol to system go-live.
What they are checking: Whether they own the full chain of start-up deliverables and their dependencies, since a late database build delays first patient in for everyone.
How do you review a draft protocol to spot data collection problems before CRFs are built?
What they are checking: Whether they catch data collection problems at the protocol stage, when fixes are cheap, instead of discovering them after CRFs and systems are built.
What makes a CRF design good, and how do you keep CRFs consistent across studies in a therapeutic area?
What they are checking: Whether they apply data standards consistently across studies, because consistent CRFs make pooling, review and submissions far easier for the whole therapeutic area.
What operational metrics would you track to know your start-up work is on track?
What they are checking: Whether they track leading indicators that reveal trouble early, rather than only reporting milestones after they have already been missed.
How do you decide on a data surveillance approach for a study?
What they are checking: Whether they tailor data review to the real risks of each study, focusing effort on critical data instead of checking everything equally.
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Practice this out loudThe full job description
As published by AbbVie.