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What steps would you take to create a reliable model for detecting bad sellers on the Zalora marketplace? How do you plan to track its performance and determine its effectiveness?

Zalora

4 years ago

ML Knowledge

Could you elaborate on the qualities that differentiate neural networks from support vector machines, and what kinds of issues they are each suited to tackle?

Zalora

Infineon

SAP Concur

4 years ago

Describe a time when you took an unpopular decision.

Data ScientistTechnical Program ManagerSoftware EngineerProduct Manager

Zalora

Pristyn Care

Ofbusiness

+8

As a QA leader I had to choose between run a full check of a new functionality that wasnt very popular or use or let the change pass over with the aprove of the PM for some time saving. I choose run the full check, take a few days more, but when the functionality goes to production it works well, without any bugs.

4 years ago

ML Knowledge

Can you describe the different types of sampling techniques?

Zalora

Razorpay

Grafana Labs

There are various sampling techniques that can be used depending on the use cases. Random sampling, Stratified sampling, cluster sampling, systematic sampling, snowball sampling

4 years ago

What is your method for analyzing data, from data collection to communicating the results?

Zalora

Redfin

Stitch Fix

4 years ago

Statistics

Describe what p-value is and its importance in statistical research.

Zalora

ASML

ZocDoc

5 years ago

I want you to share a lesson you learned from a major failure at work.

Data ScientistData Science ManagerEngineering ManagerUX Designer

Zalora

Blackbaud

Seagate

5 years ago

Tell me a high-level overview of your background. What experience is most relevant to Zalora

Zalora

5 years ago

Where did you face a challenge in a previous duty where you had a great deal of responsibility?

Data ScientistBackend EngineerProduct ManagerSoftware Engineer

Zalora

MyGlamm

HubSpot

+7

In my role leading the launch of that new customer onboarding system, the pressure was really on. We had that tight three-month window and a lean team to build something that would be the first impression for all our new users. One specific hurdle we faced was integrating our legacy customer database with this brand-new system. The data structures were quite different, and initially, the data migration process was proving to be much slower and more error-prone than anticipated. This threatened to push our launch date.

To tackle this head-on, I didn't just delegate. I dove into the technical details with the lead engineer. We mapped out the data fields meticulously, identified the key transformation rules needed, and actually prototyped a more efficient data migration script using Python. This allowed us to automate a significant portion of the process that was previously manual.

Furthermore, to ensure we were building the right thing quickly, we implemented very short feedback loops. We'd build a small piece of the onboarding flow, get immediate feedback from a small group of internal users, and iterate based on their input. For example, early on, users found a particular step in the registration process confusing. Based on their feedback, we completely redesigned that screen within a couple of days, leading to a much smoother experience.

By getting into the technical weeds to optimize the data migration and by relentlessly focusing on user feedback through rapid iterations, we not only overcame the risk of a delayed launch but also delivered an onboarding experience that was significantly more user-friendly than initially envisioned. It was incredibly rewarding to see new customers move through the system so smoothly right from day one. That success really underscored the power of combining technical problem-solving with a user-centric approach