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I applied via Referral and was interviewed before Apr 2023. There was 1 interview round.
Capital markets refer to financial markets where long-term debt or equity securities are bought and sold.
Capital markets facilitate the buying and selling of long-term financial instruments such as stocks and bonds
They provide a platform for companies and governments to raise funds for various projects
Investors can buy securities in the primary market or trade them in the secondary market
Capital markets play a crucial ...
Financial markets are platforms where buyers and sellers trade financial assets such as stocks, bonds, commodities, and currencies.
Financial markets provide liquidity for investors to buy and sell assets.
They play a crucial role in determining the prices of assets based on supply and demand.
Examples of financial markets include stock exchanges like NYSE and NASDAQ, bond markets, and foreign exchange markets.
I rate my knowledge on industry analysis as advanced.
I have extensive experience conducting industry research and analysis.
I am proficient in using various tools and techniques for industry analysis, such as SWOT analysis, Porter's Five Forces, and PESTEL analysis.
I have a track record of successfully identifying industry trends and opportunities, which have helped drive strategic decision-making in previous roles.
posted on 28 Oct 2024
I am very comfortable with commuting to Noida.
I have been commuting to Noida for the past 5 years and am familiar with the routes.
I am open to using public transportation or driving my own vehicle for the commute.
I have researched the commute times and am prepared for any potential delays.
Bias-variance tradeoff is a key concept in machine learning where a model's ability to generalize is balanced with its complexity.
Bias refers to the error introduced by approximating a real-world problem, while variance refers to the model's sensitivity to fluctuations in the training data.
High bias can lead to underfitting, where the model is too simple to capture the underlying patterns in the data.
High variance can ...
Bias is error due to overly simplistic assumptions in the learning algorithm, while variance is error due to too much complexity.
Bias is the error introduced by approximating a real-world problem, which can lead to underfitting.
Variance is the error introduced by modeling the noise in the training data, which can lead to overfitting.
High bias and low variance can result in underfitting, while low bias and high variance...
posted on 28 Oct 2024
I am very comfortable with commuting to Noida.
I have been commuting to Noida for the past 5 years and am familiar with the routes.
I am open to using public transportation or driving my own vehicle for the commute.
I have researched the commute times and am prepared for any potential delays.
Bias-variance tradeoff is a key concept in machine learning where a model's ability to generalize is balanced with its complexity.
Bias refers to the error introduced by approximating a real-world problem, while variance refers to the model's sensitivity to fluctuations in the training data.
High bias can lead to underfitting, where the model is too simple to capture the underlying patterns in the data.
High variance can ...
Bias is error due to overly simplistic assumptions in the learning algorithm, while variance is error due to too much complexity.
Bias is the error introduced by approximating a real-world problem, which can lead to underfitting.
Variance is the error introduced by modeling the noise in the training data, which can lead to overfitting.
High bias and low variance can result in underfitting, while low bias and high variance...
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