Associate Scientist, Data II
Posted Aug 18, 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
How do you document a graph model so someone can query it without asking you?
What they are checking: Documentation that makes the asset usable.
This is an Associate Scientist, Data II in Information Research at North Chicago, focused on knowledge graph engineering. What do you want to know about who consumes the graph today and how mature it is?
What they are checking: Checking whether the asset has real users before joining to build it.
What would you do
How do you check data quality in a graph, where a wrong relationship is harder to spot than a wrong value?
What they are checking: The data quality duty in a structure that hides errors.
A scientist wants a connection in the graph that the source data does not actually support. How do you handle it?
What they are checking: Holding the model's integrity against a user's expectation.
What makes a knowledge graph genuinely useful rather than an interesting diagram?
What they are checking: Whether they judge the work by what it enables.
About the work
This builds knowledge graphs. Walk me through designing a graph model for something concrete — say, linking compounds to targets to diseases.
What they are checking: Graph modeling on a realistic pharmaceutical problem.
What is the difference between a labeled-property graph and a resource description framework graph, and when would you choose each?
What they are checking: The two model types this posting names side by side.
Walk me through writing a query to find something two steps away in a graph. What makes it slow, and what do you do about it?
What they are checking: Query engineering including optimization, which this posting names.
How do you do entity resolution when the same thing is named three different ways in three source systems?
What they are checking: The entity resolution this posting lists as a query engineering duty.
You are building an ingestion pipeline from a relational source into a graph. What do you test?
What they are checking: Pipeline testing, which this posting names alongside building.
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As published by AbbVie.