Describe a conflicting situation you encountered on a Data Science project where a team member suggested an approach you did not approve of. What did you do in that situation?

Question

Accepted Answer

Yes, conflicting situations in data science projects are quite common, especially when team members have different perspectives on solving a problem. If I were in such a situation, I would approach it in the following way:

1. **Understand Their Perspective** – I would first listen carefully to their reasoning behind the suggested approach. Sometimes, there might be a valid point that I haven't considered.

2. **Explain My Concerns** – If I still disagree, I would clearly articulate my concerns, backed by data, logic, or past experiences. For example, if their approach has computational inefficiencies or potential biases, I would highlight those risks.

3. **Encourage a Data-Driven Decision** – Since data science is all about empirical evidence, I would propose running small-scale experiments or A/B testing both approaches to see which one performs better.

4. **Seek Team Consensus** – If the disagreement persists, I would involve other team members or domain experts to get their inputs. Collaboration often leads to a refined and improved solution.

5. **Stay Open-Minded** – If their approach turns out to be effective, I would be willing to adapt. Being flexible is key to innovation.

Accepted Answer

I mentioned that usually it always happens in data science projects give the experimental nature of the work. Then I explained how to handle the situation in a data-driven way. E.g. all conflicting but promising approaches should be experimented and the evaluation criteria should decide the best solution, viz, error metrics, cross validation, A/B testing etc.