Are you ready for an exciting adventure? Well, hold on tight because we have a special treasure map that will lead us to all the fun and exciting things we’ve been searching for!

The main objective of the Hackathon was to create a technical challenge that would encourage analysts to think outside the box and come up with innovative solutions to specific problem statements.

To achieve this, we carefully curated a set of problem statements ahead of time. During the hackathon, the problem owners, who were experts in their respective fields, actively participated to provide teams with a deeper understanding of the specific topics and the necessary context. In my case, I took on the role of a Problem Owner for a topic called “Experimentation Platform Metrics, Tests, and Visualizations.”

Before the hackathon started, I answered questions on impact and deliverables of my topic such as “What will be the impact of solving this problem? What result/outcome do you expect from the team?”

Here is an example of Deliverable for the experimentation platform topic:

This is the case when you simultaneously are the PM, Developer, and user of a product! And the final version of it will be designed by you!

We expect a demo of a EXPERIMENTS FLOW 2.0 – the tools, methods, and steps a team member should take to run a split test end-to-end. It can be a Jupyter notebook, Looker dashboard, or even a web app – the limits are only your imagination.

Take an experiment dataset, and for each point of the map, show the suggested solution an analyst/pm/developer should use. You might need to prioritize the step you would like to put the most effort into – discuss in your group and choose.

I have also created some “advertisement” to attract more heads to my topic:

If you don’t think likelihood is something you can buy in a hoodie store, it’s worth trying to apply your knowledge to a scalable solution — join Experimentation Platform team on the upcoming Hackathon

On the first day I met the rest of my team: Wisers working with data in Analytics, Data Science, and Product management roles. I particularly enjoyed how we used everyone’s strengths and perspectives to tackle the hackathon problem. On the second day every team presented their proposed solution to the other participants and to a panel of judges. This was my favourite part of the hackathon. I was curious to see the different approaches we had to solving the problems and the solutions we came up with.

All the participants got feedback from our amazing judging team, which included big shots like Mark Hunter, Head of Analytics at Wise, along with the cool cats from Engineering, Design Ops, and Product. They provided us with their expert opinions and suggestions to make our ideas even better.

The hackathon also boosted several problem solutions, including the experimentation automation mentioned earlier. As a result, our Data Science team has taken charge of building the platform, and the hackathon played a vital role in gathering the requirements for the initial version of the product. The event enabled us to establish clear next steps and drive the development process forward.

And finally, but certainly not least, the Hackathon helped to grow a more powerful analytics network. What’s more, the majority of participants expressed their excitement at having learned something new during the event, making it a truly enriching experience for everyone involved.