Tell me about a time you had to work on a project that was data intensive?

The interviewer is asking you this question to understand your experience and skills in managing, interpreting and analyzing substantial amounts of data. They also want to know how comfortable you are to handle data-intensive projects and how you ensure accuracy and efficiency in such tasks. When answering, explain the project briefly, your role, the kind of data you worked with, how you managed it, the techniques and tools you used, and most importantly, the outcome of the project.

In my previous role at XYZ Tech, I worked on a project which aimed to analyze customer behavior in order to enhance marketing strategies. As the lead data analyst, I handled large datasets which included online customer activities and purchase histories. The data was scattered and inconsistent. To manage it, I used Python for data cleaning, normalization and transformation. Then, we used Tableau to create data visualizations that helped us monitor trends and patterns. The decision-makers found these visualizations useful in making informed marketing strategies. As a result of my data analysis, our targeted marketing efforts increased our customer conversion by 35%.

While working as a data scientist at ABC Corporation, I was part of a data intensive project that involved analyzing huge amounts of IoT data for our smart home products. These data sets consisted of gigabytes of unstructured and semi-structured streaming data. It was my responsibility to validate, clean, and make this data useful for our machine learning models. I used a variety of tools including Apache Spark for distributed data processing, and MongoDB for storing and querying the data. The machine learning models I built helped us predict product failures and user behaviors with an accuracy of 90%, which significantly improved our product’s reliability and customer satisfaction.