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During final stages of testing upgraded version of core banking applications, we encountered a defect which required resolution from development team. After dev deployed the defect, I had very limited time to thoroughly retest and perform regression.

My task was to perform retesting and make sure there are no major defects after the release.

After resolution, based on my prior knowledge of the apps I grouped those testcases that had a potential impact to run automation regression to speed up the testing. While I manually tested the defects with some edge case scenarios. 

I kept the cross functional team updated about the testing progress and the risk involved if testing wasn't completed on time. 

But, I was able to mitigate the risk of not completing the testing on time and major risk of unexpected issues with the help to prior knowledge and proactive communication.

Thus, I learnt from this incident to carefully strategizing the situation and collaborative approach can help to mitigate risks.

I recently took over a team from a different tribe, my approach was to observe what the team was doing and how I could best help them. 

One of the observations was that a team member was not contributing as much as was expected and ultimately was underperforming. I raised this with the previous line manager and he validated my concerns and said that he will likely get another developing in performance review and be put on a PIP. 

After some initial conversation with the individual, discussing what I've observed and how his not meeting expectations, I was pretty sure, this person was checked out and was likely heading for a difficult performance review

However, after some further discussion and people a relationship with that individual, they opened up and start to share their difficulties they were having. This then allowed me reach out the necessary teams to get them the support they needed. 

Utlimately, they proved me wrong, as due the the hard work with both put in, his on track for a good performance review 

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While determining the best model for a machine learning application for a customer classification model, I suggested a two model approach while my team member suggested a single model approach. We discussed the pros and cons during a whiteboarding session, agreed on the two model approach and would fall back to the one model approach if the two-model approach did not drive client success. The model was successful.