Neha Jain

I cracked Microsoft in my first attempt! 🚀✌️
Here are the DSA problems I repeatedly practised before the interview:

𝟏. 𝐓𝐰𝐨 𝐒𝐮𝐦: Hashing, Arrays
𝟐. 𝐌𝐞𝐫𝐠𝐞 𝐈𝐧𝐭𝐞𝐫𝐯𝐚𝐥𝐬: Sorting, Greedy
𝟑. 𝐅𝐢𝐧𝐝 𝐅𝐢𝐫𝐬𝐭 𝐚𝐧𝐝 𝐋𝐚𝐬𝐭 𝐏𝐨𝐬𝐢𝐭𝐢𝐨𝐧 𝐨𝐟 𝐄𝐥𝐞𝐦𝐞𝐧𝐭: Binary Search
𝟒. 𝐃𝐞𝐭𝐞𝐜𝐭 𝐂𝐲𝐜𝐥𝐞 𝐢𝐧 𝐚 𝐋𝐢𝐧𝐤𝐞𝐝 𝐋𝐢𝐬𝐭: Linked List, Floyd’s Cycle Detection
𝟓. 𝐋𝐑𝐔 𝐂𝐚𝐜𝐡𝐞: HashMap + Doubly Linked List (Design, O(1) Operations)
𝟔. 𝐒𝐮𝐛𝐬𝐞𝐭𝐬: Recursion, Backtracking
𝟕. 𝐊𝐭𝐡 𝐋𝐚𝐫𝐠𝐞𝐬𝐭 𝐄𝐥𝐞𝐦𝐞𝐧𝐭 𝐢𝐧 𝐚𝐧å 𝐀𝐫𝐫𝐚𝐲: Heap (Priority Queue)
𝟖. 𝐋𝐨𝐧𝐠𝐞𝐬𝐭 𝐏𝐚𝐥𝐢𝐧𝐝𝐫𝐨𝐦𝐢𝐜 𝐒𝐮𝐛𝐬𝐭𝐫𝐢𝐧𝐠: Dynamic Programming (DP), Strings
𝟗. 𝐖𝐨𝐫𝐝 𝐁𝐫𝐞𝐚𝐤: Dynamic Programming, Trie (Optional)
𝟏𝟎. 𝐃𝐢𝐣𝐤𝐬𝐭𝐫𝐚’𝐬 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦: Graphs, Shortest Path, Priority Queue

My strategy → Less “how many questions did you solve?”
And more “how deeply do you understand what you solved?”

Because MAANG doesn’t care about the numbers, it cares about the impact. It took me months to understand this…what else could I have done?

We didn’t have any guidance but today, top SDEs like Ajay Yadava (an ex-Google engineer) are providing LIVE guidance.

FREE call with him here → https://lnkd.in/g3iiq7et

He doesn’t focus on “solve 100 problems” but more on:
✔️ How to approach unseen problems (be it DSA or System Design)
✔️ Building a solid preparation roadmap, resume, job strategy.

That’s why his cohort have a 100% placement rate! There are only 20 seats though.

#SDE #SoftwareEngineering #LeetCode #TechCareers #InterviewPrep

Cracking Paypal as a ML Engineer after being a SDE wasn’t luck, it was structured prep.

And I get asked this all the time by working professionals:

“How did you manage to switch to an ML role while working full-time?”

Here’s what actually helped me stay consistent and crack it 👇

🔺 Focused on fundamentals over chasing every new tool
→ Frameworks change, libraries evolve but core concepts don’t.
→ I kept going back to first principles instead of jumping on every new trend.

🔺 Built end-to-end projects, not just models
→ I focused on the full lifecycle: data → features → training → evaluation → basic deployment.
→ Owning the pipeline changed how I thought about problems.

🔺 Practiced explaining, not just building
→ I made sure I could explain why something works, not just implement it.
→ This made a huge difference in interviews.

🔺 Worked on real-world use cases
→ Instead of toy datasets, I tried solving practical problems.
→ It helped me think in terms of impact, not just accuracy.

So if you’re a working professional looking to grow faster & think deeper in Machine learning, I’d highly recommend checking out Bosscoder Academy.

🔗 Explore their Gen AI/ ML Engineer Course: bcalinks.com/ObwnnAA

They’ve helped 2200+ professionals transition successfully through:
💡 Structured curriculum covering Python, Maths & Stats for ML, Deep Learning, Transformers & GenAI (LLMs, Prompting, RAG Systems, Agentic AI).
💡 Industry projects with real-world datasets focused on building end-to-end AI systems.
💡 1:1 mentorship from AI/ML engineers & data scientists at top product companies.
💡 100% placement support to help you land your dream AI/ML role.


#genai #datascience #collab #tech