How do you get feedback after a data science interview?

1 Why feedback matters

Feedback is essential for learning and improving as a data scientist. It can help you understand how you performed in the interview, what the interviewer liked or disliked about your answers, and how you can showcase your skills and fit better in the next round or with another company. Feedback can also boost your confidence and motivation, as well as reduce your anxiety and frustration.

3 How to use feedback

Once you get feedback after a data science interview, it is important to use it wisely and effectively to improve your performance and prospects. Start by thanking the interviewer or the source of feedback for their time and input, and acknowledging their positive and negative points. Then, analyze the feedback objectively and critically, and identify the main themes and areas of improvement. Create an action plan to address the feedback, such as reviewing your code, studying new concepts, practicing your communication skills, or seeking mentorship or coaching. Implement your action plan and track your progress, while seeking more feedback along the way to measure your improvement and adjust your strategy. Finally, showcase your improvement and feedback in your next interview by highlighting how you have learned from your previous experience and how you have applied the feedback to your work.

4 How to deal with negative feedback

Negative feedback can be difficult to receive, yet it can also be an invaluable source of learning and growth. However, not all negative feedback is beneficial or accurate, so you must be judicious when handling it. Here are some tips for dealing with negative feedback: Don't take it personally or defensively, instead try to comprehend the viewpoint and objective of the feedback giver, and be grateful for their honesty and openness. Don't dispute or explain your actions, rather listen attentively and respectfully, and ask for clarification if needed. Don't ignore or reject the feedback, instead evaluate its significance and legitimacy, and decide what to accept and what to decline. Don't let it dishearten or deject you, instead use it as a stimulus and chance to develop and demonstrate yourself.

5 How to give feedback

Giving feedback after a data science interview can be beneficial for both you and the interviewer or the company. It can help you express your interest and enthusiasm, demonstrate your professionalism and communication skills, and build rapport and trust. It can also help the interviewer or the company improve their hiring process, assess their candidates better, and find the best fit for their role. When giving feedback, it's important to be prompt; preferably within 24 hours of the interview and before the hiring decision is made. Additionally, make sure to give feedback constructively and diplomatically, being specific and concrete with examples to support your claims. Lastly, ensure that your feedback is honest and sincere without exaggerating or minimizing your opinions.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?