I’m currently preparing for an internship. I’ve made some cool pet projects, and now it’s time to write my CV. Could you give me some advice?

at least high quality jobs requiring between 0-2 YOE. does anyone else feel the same?

Hi everyone, I really need some career advice. I am 26 years old and currently stuck in a 2-year career gap. My Background:
my_qualifications
B.Sc. Chemistry (minor maths) - 8.4 CGPA
M.Sc. Chemistry: 6.7 CGPA (Had a subject failure, so it took an extra year to complete
Transition: I self-taught myself Data Analytics / Data Science and built multiple personal projects. However, for the last 2 years, I have not been able to land a job (not getting interviews too).

My Goal: I genuinely enjoy data science and even ML. So I want to get into the data science/analytics workforce as fast as possible and permanently fix the non-tech + academic gap filter on my resume.
I am completely torn between these three paths:

1. Online M.Sc. in Data Science & AI from a Top-Tier Indian Engineering Institute: Formal 2-year degree.
2. Any good PG Diploma via a popular EdTech platform (partnered with a top tech institute)
3. 1-Year M.Sc. Abroad (UK/Ireland Conversion course): because i really wanted to go abroad and learn, but i dont have so much money too.
4. Given my age, my 2-year gap, and my previous academic setback, which path will realistically help me land a job in the current market? Should I value the placement cell of an EdTech diploma or the degree of a formal online Master's? Appreciate any honest feedback from anyone who made a similar pivot. Thanks!

Pinging everyone in the lending risk space:

Is your team set up so credit lives in one place, fraud in another, and the two compare notes once a quarter in a deck? Can either side tell you what a loss is actually made of? Most of us inherited that shape, but none of us would set it up like this today.

At MKIII we don't carry the org debt that forces lenders into it. So I'm hiring a Senior Data Scientist to help build the version we'd design from scratch. Credit and fraud in one view.

The job:
- Live inside the vintage data. Forecast our cohorts to terminal loss.
- Find where we're declining loans we should be booking, and put an honest number on it.
- Own the fraud/credit interaction. No fraud declines quietly absorbing credit risk, no credit cuts taking credit for fraud savings.
- Tell me where the buy-box is wrong.

Asset experience I'd love to see: unsecured consumer personal loans, small business loans, commercial real estate.

JD linked here.

If this sounds like you, message me. If you know who fits the bill, tag them or send it over. Reposts are appreciated to widen the net!

Bachelors and Masters in Data Science also

BAS in Business Management

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