"I’ve spent over 6 years building and scaling e-commerce products across EMEA and APAC.
At Jumia, I led product initiatives on the checkout and payments side. For example, I launched gamified promotions on PDP and checkout that improved engagement and delivered a 2.3x uplift in conversion. I also introduced automated installment payments and order cancellation flows, which not only improved user trust but also reduced complaints by 30% and lowered operational costs.
Before that, at Lazada, I work"
Rajeev K. - "I’ve spent over 6 years building and scaling e-commerce products across EMEA and APAC.
At Jumia, I led product initiatives on the checkout and payments side. For example, I launched gamified promotions on PDP and checkout that improved engagement and delivered a 2.3x uplift in conversion. I also introduced automated installment payments and order cancellation flows, which not only improved user trust but also reduced complaints by 30% and lowered operational costs.
Before that, at Lazada, I work"See full answer
"[I'm not sure whether the answer below is the best, as I have not gotten result and feedback from my interview]
Ans: I would solve by first using a VAE-style model, to create a latent space embedding that translates user description to generate images. Training would be done on the 1000 avatar images and 100000 descriptions, following this scheme:
VAE:
description -> encoder -> latent space -> decoder -> image
Q: "OK, but that means you're limiting the generated images to be only the 1000 imag"
Nick S. - "[I'm not sure whether the answer below is the best, as I have not gotten result and feedback from my interview]
Ans: I would solve by first using a VAE-style model, to create a latent space embedding that translates user description to generate images. Training would be done on the 1000 avatar images and 100000 descriptions, following this scheme:
VAE:
description -> encoder -> latent space -> decoder -> image
Q: "OK, but that means you're limiting the generated images to be only the 1000 imag"See full answer
"from typing import List
def traprainwater(height: List[int]) -> int:
if not height:
return 0
l, r = 0, len(height) - 1
leftMax, rightMax = height[l], height[r]
res = 0
while l < r:
if leftMax < rightMax:
l += 1
leftMax = max(leftMax, height[l])
res += leftMax - height[l]
else:
r -= 1
rightMax = max(rightMax, height[r])
"
Anonymous Roadrunner - "from typing import List
def traprainwater(height: List[int]) -> int:
if not height:
return 0
l, r = 0, len(height) - 1
leftMax, rightMax = height[l], height[r]
res = 0
while l < r:
if leftMax < rightMax:
l += 1
leftMax = max(leftMax, height[l])
res += leftMax - height[l]
else:
r -= 1
rightMax = max(rightMax, height[r])
"See full answer
"Make current as root.
2 while current is not null,
if p and q are less than current,
go left.
If p and q are greater than current,
go right.
else return current.
return null"
Vaibhav D. - "Make current as root.
2 while current is not null,
if p and q are less than current,
go left.
If p and q are greater than current,
go right.
else return current.
return null"See full answer
"I would recognize the factors that are causing the interference. Then i will use tools like smoothing techniques or algorithms (e.g Kalman filters for time series) which can help isolate genuine trends from noise. In testing i would employ techniqu es like A/B testing to measure interference from unrelated factors and use techniques like regression analysis to seperate the relevant factors from noise."
Trusha M. - "I would recognize the factors that are causing the interference. Then i will use tools like smoothing techniques or algorithms (e.g Kalman filters for time series) which can help isolate genuine trends from noise. In testing i would employ techniqu es like A/B testing to measure interference from unrelated factors and use techniques like regression analysis to seperate the relevant factors from noise."See full answer
"In Python, an "oops" (Object-Oriented Programming) concept refers to a programming paradigm that is based on the idea of objects and classes. OOP allows developers to model real-world concepts and create reusable code blocks through the use of inheritance, polymorphism, and encapsulation.
Here are some common OOP concepts in Python:
Class: A class is a blueprint for creating objects. It defines the attributes and behaviors that objects of that class will have.
Object: An object is an insta"
Anonymous Flamingo - "In Python, an "oops" (Object-Oriented Programming) concept refers to a programming paradigm that is based on the idea of objects and classes. OOP allows developers to model real-world concepts and create reusable code blocks through the use of inheritance, polymorphism, and encapsulation.
Here are some common OOP concepts in Python:
Class: A class is a blueprint for creating objects. It defines the attributes and behaviors that objects of that class will have.
Object: An object is an insta"See full answer
"Leetcode 347: Heap + Hashtable
Follow up question: create heap with the length of K instead of N (more time complexity but less space )"
Chen J. - "Leetcode 347: Heap + Hashtable
Follow up question: create heap with the length of K instead of N (more time complexity but less space )"See full answer