Two candidates describe Optum data analyst interviews that combine a recruiter screen with a case exercise or take-home analysis on healthcare data. Communication with non-technical stakeholders also came up.
$93K
Avg. Base Comp
$116K
Avg. Total Comp
2-3 rounds
Typical Rounds
Not reported
Process Length
Candidates describe Optum data analyst interviews as practical and healthcare-focused. One Business Data Analyst candidate reports a recruiter call, a video interview with the hiring manager, and a case exercise on evaluating performance metrics in a healthcare context, which they called the hardest part. The hiring manager asked them to describe a time they explained a complex analysis to a non-technical stakeholder, so have a concrete story ready.
A second candidate, for a Data Analyst role, reports a take-home assessment built on medical data. It came with five datasets covering patient records and insurance claims, a suggested limit of about three hours, and a free choice of coding language. Some questions were direct, such as counting patients with more than five diagnoses. Others were open-ended, such as finding the most expensive diagnosis combination. Practice joining and aggregating claims-style tables and stating your assumptions clearly.
The process was not smooth for either candidate. One described unclear skill-assessment timing and mixed signals between HR and the hiring manager. The other waited over six weeks after submitting the assessment and then withdrew when an interview did not go ahead. Ask each recruiter for the next steps and timing, and learn how Optum's analytics supports healthcare operations before you interview.
Synthesized from 3 candidate reports by our editorial team.
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| Question | |
|---|---|
| Size of Joins | |
| Sort Strings | |
| Hurdles In Data Projects | |
| Concurrent LLM Serving | |
| String Palindromes | |
| Your Strengths and Weaknesses | |
| Account Personalization Strategy | |
| Fast Food Database | |
| Experiment Validity | |
| Prime to N | |
| Weighted Keys | |
| Button AB Test | |
| Decreasing Comments | |
| P-value to a Layman | |
| Liked Pages | |
| Network Experiment Design | |
| Bagging vs Boosting | |
| Daily Retention Summary | |
| Delivery Estimate Model | |
| Detecting ECG Tachycardia Runs | |
| Google Maps Improvement | |
| Declining Applicants | |
| Random Forest Explanation | |
| Subscription Retention | |
| Valid Anagram | |
| Xgboost vs Random Forest | |
| Find Duplicate Numbers in a List | |
| Success Measurement | |
| Target Indices |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report starting with a recruiter conversation. One describes a roughly 30-minute phone screen covering background, analytics experience and salary expectations. Another describes an easygoing chat about their CV and motivations. Expect to explain why you want a healthcare analytics role.
One Business Data Analyst candidate reports a video interview with the hiring manager that went deeper into past projects and analytics approach. They were asked to describe explaining a complex analysis to a non-technical stakeholder. Not every candidate mentions this stage, so treat it as a likely but unconfirmed step.
Candidates report a technical exercise, though the format differed. One describes a case on evaluating performance metrics in a healthcare context. Another received a take-home assessment with five medical datasets (patient records and insurance claims), a one-week deadline and a suggested three-hour limit. Language choice was open, and Python was used.
Reported timelines varied. One candidate describes about three weeks end to end. Another heard nothing for over six weeks after submitting the assessment, then withdrew when a scheduled interview did not go ahead. Candidates may want to confirm next steps and timing in writing after each stage.
