One Abbott Data Analyst candidate reports a behavioral conversation, a SQL question and an hour-long spreadsheet exercise on a sales dataset, followed by slow communication. Another describes a high-level HR screen before withdrawing.
$81K
Avg. Base Comp
$95K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
The Abbott Data Analyst interview, as two candidates describe it, mixes storytelling with hands-on data work. One candidate for a Data Analytics role began with an online behavioral round with a hiring manager, who asked them to walk through a time they used data to make an important business decision. The same conversation covered visa status and salary expectations.
That candidate later faced a SQL question testing querying and data manipulation. They then joined an on-site working session with team members, where they had about an hour to answer analytics questions on a real sales dataset in a shared spreadsheet, using formulas and smart queries. They described the team as friendly and the session as collaborative.
A candidate for a Clinical Data Analyst role reports a different start: an HR screen over MS Teams without the hiring manager. It covered an in-office probationary period with the possibility of one remote day later, and a question about start date. Behavioral and technical questions were mentioned as coming later. That candidate withdrew, wanting more detail about the day-to-day work.
For preparation, have one or two concrete decision-focused data stories ready. Practice SQL and spreadsheet formulas while talking through your reasoning. Ask the team what problems the role solves. The Data Analytics candidate also describes a missed interview and silence after the final step, so confirm next steps and timelines as you go.
Synthesized from 2 candidate reports by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Abbott process.
I started the interview process with a positive impression—Abbott seemed professionally organized and the initial contact was encouraging. My first round was behavioral, conducted online with a hiring manager who asked me to walk through a time when I used data and numbers to make an important business decision. The conversation felt straightforward, and they covered logistical ground too, asking about visa status and salary expectations. I thought it went well and waited to hear back.
Then things got unclear. I was scheduled for a second round, but the interviewer didn't show up. When I followed up, they rescheduled it to a later date without much explanation. This first experience with communication gaps was a red flag, but I remained committed to the process. The next stage involved a technical component—a SQL question designed to assess my querying and data manipulation skills. It was a realistic evaluation of what the role would require.
After that technical round, I also participated in an on-site session where I worked through a data analytics exercise on a shared spreadsheet with members of the team. I was given about an hour to tackle analytics questions using formulas and smart queries on a real sales dataset. The team members were friendly and approachable, which made the working-session feel collaborative rather than adversarial.
I completed every step they asked of me and invested significant time preparing and interviewing. But as weeks passed, communication stopped entirely. I didn't receive a rejection, an offer, or even a status update. The lack of closure was harder to accept than a straightforward no would have been. A simple final decision, positive or negative, would have given me clarity and the ability to move forward. Overall, the process could have been salvaged with basic communication consistency.
Prep tip from this candidate
Prepare for both behavioral questions ("tell me about a time when...") and hands-on technical work with SQL and spreadsheet analytics—Abbott combines storytelling with practical data manipulation skills. Be ready to work through real datasets on a shared screen, and bring specific examples of how you've used data to drive decisions.
Share your own interview experience to unlock all reports, or subscribe for full access.
Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Abbott
Describing a data project and its challenges
| Question | |
|---|---|
| Digital Marketing Metrics | |
| Slow SQL Query | |
| Client Solution Pushback | |
| Vision Setting and Execution Strategy | |
| Testing Constraints | |
| Stakeholder Communication | |
| Your Strengths and Weaknesses | |
| Presentations and Insights | |
| Data Cleaning Experiences | |
| Linear Regression Parameters | |
| Meta in an Emerging Market | |
| 2nd Highest Salary | |
| Duplicate Rows | |
| P-value to a Layman | |
| Minimize Wrong Orders | |
| Decision Tree Evaluation | |
| Causal Email Journey | |
| Duplicate Product Names | |
| Target Indices | |
| 85% vs 82% | |
| Classification and Regression | |
| DDoS Attack Response | |
| String Palindromes | |
| Credit Card Fraud Model | |
| Pipeline Transformation Failures | |
| Distributed Authentication Model | |
| Why Do You Want to Work With Us | |
| Forecasting Revenue | |
| Marketing Workflow Optimization |
Synthesized from candidate reports. Individual experiences may vary.
One Clinical Data Analyst candidate reports an initial HR screen over MS Teams covering an in-office probationary period and start date. The hiring manager did not attend and the session stayed high level, so come ready with your own questions about the role.
One Data Analytics candidate reports an online behavioral round where the hiring manager asked about a time they used data and numbers to make an important business decision, and also asked about visa status and salary expectations. Prepare a specific example that shows the problem, your analysis and the outcome. Scheduling may slip, as this candidate's interviewer missed a later session.
The same candidate reports a SQL question meant to assess querying and data manipulation skills, described as realistic for the job. Practice writing queries on business-style tables, such as joins, aggregation and filtering, and explain your approach out loud as you work.
The same candidate describes about an hour working with team members on a shared spreadsheet, answering analytics questions on a real sales dataset using formulas and smart queries. The tone was collaborative. Practice lookups, pivots and clear commentary on what the numbers mean. This candidate also reports no follow-up after the final step, so ask about timing.
