Please share with me a time you failed at work.

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Anonymous · Accepted Answer

At my previous role, I was leading a fraud detection AI model deployment for a
financial services client. The model aimed to reduce fraudulent transactions by
25% while maintaining a low false positive rate.
I was confident in the model’s performance in the test environment, but when we
deployed it in production, we noticed an unexpected spike in false positives.
Transactions flagged as fraudulent were legitimate, causing disruptions for
customers and increasing manual review work. To resolve this, I:
 1. Investigated the issue by comparing training and production data.
 2. Identified data drift—the model was trained on historical data, but recent
    transaction patterns had changed.
 3. Worked with data engineers to implement real-time data monitoring.
 4. Retrained the model with up-to-date data and adjusted hyperparameters to
    improve generalization.

This fix reduced false positives by 25%, improved fraud detection accuracy, and
ultimately saved $500M in operational savings across multiple initiatives. This
experience taught me the importance of continuous model monitoring and
proactively accounting for data drift. Now, I always incorporate data validation
pipelines and feedback loops into AI deployments to prevent similar issues.

Anonymous · Answer

I was asked to implement dot1x within a month of joining Pluribus networks. I
was so excited that i have done the design, got it reviewed, coded, reviewed,
tested.  And ask QA to pick the feature. And I faced a lot of problems with
upgrade downgrade scenarios,  Unique cfg replication issues.  That was the first
time I had such failures. 
So I immediately, accepted my fault to my manager and asked for a day or two to
write new UT plan, now reviewed that with QA.  Verified, and did all fixes
needed.  Result: Documentation, UT, Prototyping take a lot of value equal to
that Implementation, Design

Please share with me a time you failed at work.

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