The most popular e-commerce site in India, Flipkart, has grown to become the country’s top online market. According to the company, the “Data Science team at Flipkart is on a mission to establish systematic intelligence across Flipkart products and the overarching ecosystem.”
Massive amounts of rich data are gathered daily, including a fantastic mix of numbers, organized and unstructured data, and statistics based on images and audio. These data are destined to change how Flipkart approaches retail in the future.
At Flipkart, data scientists use specialized methods like classification, regression, clustering, matrix factorization, graphical models, tree-based models, network and graph algorithms, topic modeling, image processing, deep learning, and natural language processing. For even bigger endeavors, these are used on enormous databases.
You can register in Learnbay’s data science course in Chennai, if you are interested in becoming a data scientist at MNC firms.
How does Flipkart do data scientist interviews?
The two phases of the interview procedure at Flipkart are as follows:
Phase 1 pre-onsite interview
- Interviewer Discussion
- Screening of Technical Phones
Phase 2 In-person interview
- Practical ability
- Discussion of data science and mathematical modeling in depth
- Interview about culture and team fit.
Rounds of Flipkart’s data scientist hiring process
First Round: Pre-onsite interview
The shortlisted candidate will be interviewed over the phone in this phase. A Flipkart recruiter or hiring manager will contact the candidate by phone. You’ll be asked questions regarding your resume and about your prior experiences, projects, and expectations for the position.
In addition, if this position is filled, you will be given a tour of the job description and informed of the organization’s expectations. Also covered in detail will be the hiring procedure.
Following that, you’ll be on the phone with a possible colleague who will quiz you on technical issues and gauge your degree of expertise. Being a technical round, the emphasis is on prior work experience, job eligibility, and a few problem-solving exercises pertinent to the position you are interviewing for.
You might have to go in-depth when explaining one of your past projects to the interviewer. Thus, your strengths and weaknesses will be considered in your evaluation.
Second Round: In-person interview
The onsite interview’s presentation portion is where you can discuss any former project you have worked on with the interviewer. Those who have substantial work experience or research experience are eligible for this round.
This is important since it sets up the rest of the technical conversation you will have throughout this interview. You must submit your presentation’s title and abstract in advance. The session will be participatory, so be ready for that.
Normally, the presentation lasts 45 minutes, and the Q&A portion lasts 15 minutes.
A dataset and related problem statement will then be given to you. Your capacity to comprehend the issue and find a solution via comprehension will be assessed.
The information and best-practice solution modeling. You will ultimately be required to submit the code. It will take about an hour to complete this round.
The next round includes a thorough treatment of mathematical modeling and data science. This round aims to comprehend your thought process by thoroughly discussing your method for resolving a practical issue.
20 Questions that will you help ace data scientist interview
It is anticipated that the candidate is knowledgeable about basic machine learning concepts such as bias-variance tradeoff, error analysis, cross-validation, train/test protocols, evaluation metrics, and procedure. Examples of them are as follows:
- What is Natural Language Processing?
- Why is the study of natural language important?
- How do you make an effective decision tree?
- What is the decision tree method’s significance?
- What is a decision tree method?
- Describe Big Data.?
- How are you going to extract the data from the given data set?
- What are the most typical problems with information retrieval?
- How should imbalanced data be handled?
- Have you made any contributions to open-source projects?
- How does root cause analysis work?
- Describe the function of the group function in SQL. Provide a few instances of how a group functions.
- What is a Gaussian distribution in data science and how is it applied?
- What distinguishes underfitting and overfitting in particular?
- What aims does A/B testing pursue?
- What are the constraints placed on a linear model or regression?
- How does collaborative filtering work?
- An API definition What purposes serve APIs?
- Out of five million search queries, how would you choose a representative sample?
- How do a depth-first search (DFS) and breadth-first search (BFS) vary?
You can navigate the Flipkart interview procedure with the help of this guide. Click here to learn more about the data science course in pune if you want to start learning from the ground up. .