Stage 1: HR/Recruiter Screening Call (15–30 minutes)
Objective: Assess communication skills, motivation, and culture fit.
Questions:
Why are you interested in this role?
Tell us about a recent data project you worked on.
What are your salary expectations and notice period?
Stage 2: Technical Assessment (Take-Home or Online Test)
Objective: Evaluate coding ability, data wrangling, and problem-solving skills.
Format: Could include a case study or dataset analysis with deliverables (code, notebook, and brief report).
Typical Tasks:
Data cleaning and EDA.
Feature engineering.
Model building (e.g., regression, classification).
Result interpretation and communication.
Stage 3: Technical Interview ( 45 minutes)
Objective: Deep dive into technical knowledge and approach.
Topics:
Python, SQL queries, Pandas, NumPy.
Machine learning algorithms and model evaluation.
Probability, statistics, and hypothesis testing.
Business case discussion or live coding.
Sometimes includes a whiteboard/diagramming session.
Stage 4: Case Study or Business Problem Discussion
Objective: Assess analytical thinking and ability to connect technical work to business outcomes.
Example Format:
Present a problem (e.g., churn prediction or sales forecasting).
Ask candidate to explain how they would approach the solution, what data they would need, potential pitfalls, etc.
Stage 5: Final Interview / Cultural Fit
Objective: Gauge alignment with company values and team dynamics.
Interviewers: Team lead, manager
Topics:
Past experiences and team collaboration.
Ethical considerations in data use.
Career aspirations and long-term goals.
