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AB InBev India
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I applied via Approached by Company and was interviewed in Feb 2022. There were 3 interview rounds.
Forecasting in time series involves predicting future values based on past data.
Identify the trend, seasonality, and any outliers in the data
Choose an appropriate forecasting method such as ARIMA or exponential smoothing
Split the data into training and testing sets to evaluate the accuracy of the model
Adjust the model parameters and re-evaluate until satisfactory results are achieved
Forecasting related case studies
I applied via campus placement at Indian Statistical Institute (ISI), Kolkatta and was interviewed before Dec 2021. There were 4 interview rounds.
Two coding questions on basic Dynamic programming. Difficulty : Easy to Medium Easy
Case Stydy type questions :
How do you place beer in the shelves ( also beer comes in cans, 6 packs, q2 packs, bottles , large bottles ) , given shelves can accomodate only a finite volume and you have the paat sales details for all brand and types of beer and the profits.( My approach : Analogous to integer knapsack )
You have a new beer brand with no previous sales data. Do you put it in the shelves ? If so, how much , and how do you determine if the beer will sell good in the near future after , say, 2 months of you keeping it in the shelves. You have to take into account that many people will be biased against a new brand because the brand isnt establiahed.
I applied via campus placement at Indian Institute of Technology (IIT), Chennai and was interviewed in Dec 2016. There were 6 interview rounds.
I applied via campus placement at Indian Institute of Technology (IIT), Chennai and was interviewed in Dec 2016. There were 6 interview rounds.
AB InBev India interview questions for designations
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Autocorrelation is a statistical concept that measures the relationship between a variable's current value and its past values.
Autocorrelation is the correlation of a signal with a delayed copy of itself.
It is used to detect patterns or trends in time series data.
Positive autocorrelation indicates a positive relationship between current and past values, while negative autocorrelation indicates a negative relationship.
F...
Linear regression assumptions include linearity, independence, homoscedasticity, and normality.
Assumption of linearity: The relationship between the independent and dependent variables is linear.
Assumption of independence: The residuals are independent of each other.
Assumption of homoscedasticity: The variance of the residuals is constant across all levels of the independent variables.
Assumption of normality: The resid...
P value is a measure used in hypothesis testing to determine the significance of the results.
P value is the probability of obtaining results at least as extreme as the observed results, assuming the null hypothesis is true.
A small P value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, leading to its rejection.
A large P value (> 0.05) suggests weak evidence against the null hypothesis, lead...
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