Describe your most and least engaging work

A strong answer shows self-awareness, motivation, and role fit. ### What the interviewer is testing They usually want to learn: - what genuinely motivates you, - whether your preferred working style matches the team, - whether you can discuss less enjoyable work without sounding negative, - and whether you understand the realities of a Data Scientist role. ### Recommended structure Use a simple 3-part structure. 1. **Most engaging work** - Pick 1 to 2 concrete examples. - Explain why they were engaging: ambiguity, stakeholder impact, experimentation, modeling, product strategy, or collaboration. - Show what you did and what changed because of your work. 2. **Least engaging work** - Choose something real but not fatal to the job. - Frame it as a preference, not a complaint. - Good examples: repetitive reporting with no decision-making impact, poorly scoped projects, unclear ownership, or work where success criteria constantly changed. - Then show how you handled it constructively. 3. **Tie back to the role** - End by explaining why this role seems like a strong match. - Mention the team, product, collaboration model, or problem space. ### What makes a good answer A strong Data Scientist answer often highlights engagement with: - solving ambiguous product or business problems, - translating data into decisions, - partnering with product, engineering, and design, - running experiments or building measurement frameworks, - and owning work that influences roadmap or strategy. For less engaging work, a mature answer might say: - you are less energized by manual recurring analysis that never informs decisions, - or by projects with no clear objective function, - or by work that is purely reactive and never closes the loop with outcomes. That is much better than saying you dislike data cleaning, stakeholder communication, or operational detail, because those are normal parts of most DS jobs. ### Example answer outline - **Most engaging:** I am most engaged when I work on ambiguous product questions where data can materially change a decision. For example, I enjoyed projects where I had to define success metrics, identify behavioral patterns, and partner with product and engineering to recommend a launch or prioritization decision. - **Least engaging:** I am least engaged when analysis becomes a reporting exercise with no clear decision owner or downstream action. I can still do that work well, but I am more motivated when there is a clear hypothesis, decision, or product question attached. - **Role fit:** That is why this role is appealing: it seems to sit close to product decisions, user behavior, and cross-functional strategy. ### Common mistakes - Being too generic: saying only that you like interesting problems. - Sounding negative: criticizing past managers, teammates, or business functions. - Naming a core job responsibility as your least favorite part. - Giving preferences without evidence from real examples. ### Interview tip The best answers balance honesty and fit. You do not need to pretend to love every task. Instead, show that you understand your strengths, manage lower-preference work professionally, and thrive in environments similar to the one you are interviewing for.