Journey to Cracking a Data Scientist Job with 17 LPA as a Fresher

Introduction

Speaker: Nitish
Guest: Tarun Chauhan
Objective: Share Tarun’s journey from a Tier-3 college to landing a Data Scientist job with a 17 LPA package.
Key Message: Proper planning, execution, and hard work can help achieve dream jobs regardless of background.

Background: Freshers from GL Bajaj Institute of Technology and Management, pursuing B.Tech in CSE with specialization in AI & ML.
Year: Fourth-year student.
Achievement: Cracked a Data Scientist job with a 17 LPA package.

Current Market Situation: Tough job market for freshers especially in Data Science and Machine Learning.
Initial Doubts: Uncertainty about job prospects in the field due to market conditions and freshers’ sentiment.

High School to College Transition: Interest in Computer Science, not initially focused on Data Science or AI/ML.
First Encounter with AI/ML: During college research on specializations.
Advice from Seniors: Dive into AI/ML field despite job prospects uncertainties.

Timing: Started learning Python in the second year.
Learning Strategy: Watched multiple roadmap videos to understand the essential topics for Data Science.
Resource Struggle: Initially struggled with finding good resources for Python.

SQL Learning: Started SQL from Nitish’s YouTube videos.
Roadmap Followed: Used 100 days of machine learning playlist diligently.
Focused Learning: Stopped diverging to multiple sources after finding a comprehensive resource.

Projects: Built regression, classification projects, and a forecasting project.
Major Project: Enhanced movie recommendation system using Python, HTML, CSS, JavaScript.
Internships: Gained industry experience through multiple internships (Fynas Education, Innovatec Research Lab).

Initially Used Platforms: LinkedIn, Internshala, n.com, unstopped.
Cold Emails: Sent personalized requests and resumes to employees in target companies.
Persistent Efforts: Maintained rigorous application and follow-up routine for six months.
Interviews: Attended 15 interviews before landing the job with Monotype.

Company Profile: Provides designing services, mainly fonts like Times New Roman, Roboto.Uses generative AI for font creation.
Role: AI/ML Engineer focusing on computer vision and font modeling.

Coding Round: Included DSA questions — subarray sum, median of sorted arrays, finding duplicates, sorting functions.
SQL Round: Covered DB basics, window functions, and complex SQL queries.
Data Science Round: Focused on statistical concepts, hypothesis testing, model building, and ML algorithms.
ML/DL Round: Detailed questions on linear regression, logistic regression, decision trees, SVM, CNNs, activation functions.
Importance of Examples: Using detailed examples in answers impressed interviewers.

DSA Importance: Continue practicing DSA as it is crucial for interviews, even in Data Science roles.
Mathematical Foundation: Understanding the math behind algorithms can differentiate candidates.
Detailed Note-taking: Writing detailed notes in one's own language aids memory and understanding.
Persistent Job Hunting: Continuous effort in job applications and maintaining an updated resume.
Project Depth: Conceptually sound and scoped projects can impress interviewers.

Speaker’s Impression: Extremely impressed with Tarun's detailed preparation and persistence.
Final Message: With the right strategy and persistence, securing a dream job in Data Science is achievable even from a Tier-3 college.

Notes: Make comprehensive, personalized notes for quick revision and deep understanding.
DSA Practice: Prioritize DSA practice regularly, especially for technical interviews.
Continuous Learning: Stay updated with latest trends and keep improving your skills.

Encouragement: Tarun’s journey is inspiring for students aiming for a career in Data Science.
Next Steps: Keep following a structured roadmap and stay persistent in job hunting.