Scenario:
During my time as a data scientist at my previous company, I was leading a project working with a team of analysts and developers to build a predictive model for a major client. Midway through the project, we found out that the client had changed their requirements. They wanted us to use a new set of data which was significantly larger than the one we were previously using, and they wanted us to deliver the model three weeks earlier than we had originally planned.
Decision-making process:
This was a tough decision because it meant we had to significantly alter our approach and timeline. I approached the situation by first gathering all the necessary information - I spoke to my team members, looked at the feasibility of the new data set and deadlines, and analyzed the possible outcomes of different scenarios.
I then held a meeting with my team to discuss the options we had and to get their input. We debated the pros and cons of each option, and I encouraged everyone to speak up and share their opinions. I used their feedback to make an informed decision.
Outcome:
After carefully considering all factors, I decided to inform the client that we couldn't meet their request for an earlier deadline. Instead, we proposed a revised timeline that would allow us to incorporate the new data set and deliver a high-quality product. The client was initially disappointed but ended up accepting our proposal when we explained the potential risks of rushing the model and the benefits of following a more thorough and thought-out approach.
Ultimately, the result was a successful project that met the client's needs and exceeded their expectations. We were able to build a robust predictive model while also maintaining the quality of our work.
Relevant citations: