Free · takes a minute · your resume fills your profile
CoreHRx never asks candidates for money. If anyone does, .
Role at a glance
Experience
7–11 years
Education
Bachelor's degree
Must-have skills
Data AnalysisDeep LearningMachine LearningPythonSQLForecasting
How well do your skills match this job?
Sign in to see your fit: the skills, experience and location this job asks for, checked against your profile.
Job description
Strong expertise in Time Series Forecasting, Regression, Classification, and Clustering techniques, with the ability to design, review, and recommend analytical solutions.
Hands-on experience in Machine Learning and Deep Learning model development and deployment.
Demonstrated expertise in at least 1-2 business analytics domains, such as:
Pricing & Promotion Optimization
Next Best Recommendation
Sales Spend Optimization
Supply Chain Optimization
Experience working with cloud-based analytics platforms such as Azure, AWS, or GCP.
Excellent communication, presentation, and stakeholder management skills, with the ability to translate complex analytical insights into business recommendations.
Strong knowledge of DBMS concepts and advanced SQL skills.
Ability to evaluate, innovate, and implement advanced analytical techniques to enhance business value and client outcomes.
Demand Forecasting
Customer Segmentation
Customer Value Management (CLV)
Marketing Mix Modeling (MMM)
Deep proficiency in Python or R for advanced analytics and model development.
Solid understanding of statistical concepts, data analysis, and data wrangling using SQL, databases, and Excel.
Graduate/Postgraduate from Tier-I or Tier-II institutes; Master's preferred in Statistics, Mathematics, Operational Research, Economics, or related quantitative disciplines.
7 to 11 years of experience in Data Science, with at least 70% relevant experience in analytics, statistical modeling, machine learning, or AI.
Key skills
Required
Data AnalysisDeep LearningMachine LearningPythonSQLForecastingR
This guide is for freshers and working analysts in India preparing for data analyst interviews at product companies, IT services firms, banks, e-commerce and fintech startups. It has 60 questions with model answers in three levels: the basics every fresher is asked, the real-world problems analysts with one to four years face (wrong joins, messy data, conversion drops), and the senior questions on experiment design, metric traps and leading decisions with data. Every SQL query and every number in the answers was run or calculated. Read the question, answer it aloud in your own words, then compare, and take the mock test to practise under time.
Questions you may be asked
What does a data analyst do, and what does a typical day look like?
How is a data analyst different from a data scientist and a data engineer?
What is the difference between WHERE and HAVING? Show it with a query.
What is the difference between an INNER JOIN and a LEFT JOIN, and why can a customer count drop after a join?
