Interview Questions

Esther S. - "Things can get chaotic a lot of times where you're pulled into endless meetings with slack pings, emails, tags from Jira/confluence as well as adhoc asks from leadership team. Based on urgency/severity of tasks at hand, I would have my own board for tracking my tasks and set priorities and due dates. For instance, I would create my own Trello board of things that need to get completed in specific date, block out time in my calendar to work on them (as well as putting reminders on my calendar 3 d"See full answer

Himanshu G. - "First, I’d start by checking the alignment of each idea with our core business goals. If any idea doesn't directly contribute to those goals, I’d deprioritize or eliminate it upfront. Next, I’d use a scoring model like RICE (Reach, Impact, Confidence, Effort), especially because effort is a critical factor when resources are limited. This gives us a structured and quantifiable way to rank the ideas. Once we have a prioritized list based on scores, I’d take it a step further and evaluate key as"See full answer

Dhruv M. - "Thankyou for asking me this answer. What makes me unique in data analytics is my ability to blend technical skills with a strong business mindset. I don’t just focus on building dashboards or running analyses-I always tie the insights back to real business impact. During my internship at Quantara Analytics, for example, I didn’t just track supplier KPI's. I redesigned the reporting process, which cut manual work by 60% and improved decision-making. I’m also proactive about learning tools like Po"See full answer

Emiliano I. - "Look for the main variables and see if there differences in the distributions of the buckets. Run a linear regression where the dependent variable is a binary variable for each bucket excluding one and the dependent variable is the main kpi you want to measure, if one of those coefficients is significant, you made a mistake. "See full answer

Lucas G. - "Type I error (typically denoted by alpha) is the probability of mistakenly rejecting a true null hypothesis (i.e., We conclude that something significant is happening when there's nothing going on). Type II (typically denoted by beta) error is the probability of failing to reject a false null hypothesis (i.e., we conclude that there's nothing going on when there is something significant happening). The difference is that type I error is a false positive and type II error is a false negative. T"See full answer

Lucas G. - "A confidence interval gives you a range of values where you can be reasonably sure the true value of something lies. It helps us understand the uncertainty around an estimate we've measured from a sample of data. Typically, confidence intervals are set at the 95% confidence level. For example, A/B test results show that variant B has a CTR of 10.5% and its confidence intervals are [9.8%, 11.2%], this means that based on our sampled data, we are 95% confident that the true avg CTR for variant B a"See full answer

Lucas G. - "Null hypothesis (H0): the coin is fair (unbiased), meaning the probability of flipping a head is 0.5 Alternative (H1): the coin is unfair (biased), meaning the probability of flipping a head is not 0.5 To test this hypothesis, I would calculate a p-value which is the probability of observing a result as extreme as, or more extreme than, what I say in my sample, assuming the null hypothesis is true. I could use the probability mass function of a binomial random variable to model the coin toss b"See full answer

Sri H. - "First of all, are some of these hypothetical questions? Doesn't sound like a recent Meta Analytical/Execution and rather is a strategy question but you can lead someone from a decision to an execution, I guess!"See full answer