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Hi, my name is XXXX and I have a total of 7 years of experience across product and analytics teams. Currently, I work as a Product Manager at Gameskraft, where I collaborate within two pods: Gameplay Experience (focused on improving user experience during gameplay) and Monetisation (focused on increasing company revenue through new initiatives). I work closely with business, design, and tech teams to deliver outcomes. Additionally, I manage a team of two data analysts who report directly to me. I have also been exposed to AI projects, including developing an AI conversational bot for our organisation to reduce customer support costs.

l, I initially built automation scripts for trade reconciliations and reporting using standalone Python and UNIX-based cron jobs. These worked well for smaller data volumes, but as our operations scaled across global teams in London, Hong Kong, and the Bahamas, performance and maintainability became a bottleneck.

I realized I needed to adapt my workflow to better support large-scale, distributed processes. So, I changed how I worked by migrating critical pipelines from PostgreSQL and flat scripts into a more robust ETL framework using Databricks and Delta Lake. I restructured batch processes into streaming jobs and modularized the Python code to be production-grade. I also started using Kafka and Kubernetes to improve system reliability and orchestration.

“One night flying into Newark, we were on final approach after a long day with gusty winds, turbulence, and low visibility. The captain was flying, and I was pilot monitoring, keeping an eye on airspeed, alignment, and anything that might require a callout. Everyone was ready to be on the ground — the passengers, the crew — it had been a long day, and the momentum was to just get the airplane landed.

As we crossed the runway threshold, the airplane hit a strong burst of turbulence and refused to settle. In that split second, I knew what had to be done, but I also knew it wouldn’t be the popular choice. A go-around meant a delay, frustrated passengers, and possibly second-guessing from the crew. But safety has to come first, so I immediately called ‘Go-around.’ The captain executed it smoothly, and we climbed away, briefed again, and landed safely on the second attempt.

Afterward, the captain thanked me for speaking up and said he completely agreed with the decision. It reinforced for me that as pilots, we can’t let convenience or the desire to finish cloud our judgment. Sometimes the right call isn’t the popular one, but it’s always the right call when it keeps everyone safe.”

From the data, I need to know how research questions could be extracted from a high-dimensional dataset. Once which model should be used is decided, then I feed the dataset into the machine learning model. The problem is that in a high-dimensional dataset, the number of samples is smaller than the predictors, the control variables. It is important to drop the unrelated variables, and make sure the model predicts well with the given dataset.

There was I time I had to choose between technologies for our clusters: nomad or kubernetes. I did not know back then that one of them would become so much popular than the other, but because of a good research I was able to pick better option