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I applied via Referral and was interviewed in Nov 2024. There were 2 interview rounds.
I applied via Referral and was interviewed in Jun 2023. There were 2 interview rounds.
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I applied via Referral and was interviewed in Mar 2020. There were 5 interview rounds.
Implemented data-driven strategies to increase revenue by 15% in previous company.
Developed predictive models to optimize pricing strategies
Identified key customer segments for targeted marketing campaigns
Automated data collection and analysis processes for efficiency
Collaborated with cross-functional teams to implement data-driven decisions
I applied via Campus Placement and was interviewed in Sep 2023. There was 1 interview round.
KPI's for the ecommerce industries
posted on 13 Jun 2025
I appeared for an interview before Jun 2024, where I was asked the following questions.
posted on 29 Dec 2024
It is nice to do work
It helps in understanding the 5
It helps a lot in company
Effective work involves planning, execution, analysis, and continuous improvement to achieve goals efficiently.
Set clear goals: Define what you want to achieve, e.g., completing a project by a specific deadline.
Plan your tasks: Break down your work into manageable tasks, like creating a timeline for data analysis.
Prioritize: Focus on high-impact tasks first, such as analyzing key metrics that drive business decisions.
U...
The goal of a Data Analyst is to analyze data to extract valuable insights and make data-driven decisions.
Identify trends and patterns in data
Create visualizations to communicate findings
Provide actionable recommendations based on data analysis
posted on 7 May 2024
I applied via Naukri.com and was interviewed in Jun 2023. There were 3 interview rounds.
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I applied via Referral and was interviewed in Nov 2019. There were 5 interview rounds.
Analyzing customer purchase data to identify trends and improve sales strategies.
Collect data from sales transactions to analyze customer behavior.
Use tools like SQL and Python for data cleaning and analysis.
Identify top-selling products and seasonal trends to optimize inventory.
Create visualizations using Tableau to present findings to stakeholders.
Recommend targeted marketing strategies based on customer segments.
Implemented a machine learning model to predict customer churn using advanced algorithms
Developed a predictive model using logistic regression, random forest, and gradient boosting
Utilized feature engineering techniques to improve model performance
Integrated the model into the company's CRM system for real-time predictions
based on 2 interview experiences
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Oracle
Amdocs
Automatic Data Processing (ADP)
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