1. Application Submission: Submit your resume and cover letter online or via email.
2. Screening:
HR Screening: A short phone or video call to assess basic qualifications, experience, and salary expectations.
Aptitude Test (optional): Tests for skills relevant to the job (e.g., technical, logical, or verbal).
3. Technical/Functional Round: In-depth discussion on your technical skills, problem-solving abilities, or functional expertise.
4. Managerial/Behavioral Round: Questions about work ethic, teamwork, leadership, and past experiences.
5. Final Round (optional): Could include interviews with senior management or stakeholders.
6. Offer and Negotiation: Receive the offer, negotiate if needed, and finalize terms.
7. Background Check: Verification of details like employment history, education, etc.
8. Onboarding: Join the company and complete necessary formalities.
Interview questions [1]
Question 1
1. What is the difference between supervised and unsupervised learning?
2. Explain overfitting and underfitting. How can you prevent them?
3. What are the key steps in a data science project?
Statistics and Probability:
4. Explain p-value and its significance in hypothesis testing.
5. What is the central limit theorem, and why is it important?
6. How do you check if a dataset is normally distributed?
The interview was based on the assignment, after then was a small coding session on recurrent neural networks and then was the HR interview. The recruiters were very humble and supportive .