Profile Review and Suggestions

[papashango]

Hello everyone,

I'm seeking some candid feedback on my profile for MFE applications and would greatly appreciate any insights on its strengths and weaknesses. I'm B.E Computer Science grad from a Tier 1 university in India, who graduated in June 2025 with a CGPA of 8.06/10. I also have a minor in Finance.

Here's a summary of my background:

Academics:

• Degree: Bachelor of Engineering in Computer Science
• CGPA: 8.06/10
• Relevant Coursework: Data Structures and Algorithms, Operating Systems, Database Systems, Computer Networks, Foundations of Data Science, Machine Learning, Graph Mining, Probability & Statistics, Mathematics I, Mathematics II, Mathematics III, Principles of Economics, Business Analysis & Valuation, Derivatives & Risk Management, Fundamentals of Finance and Accounting, Securities Analysis & Portfolio Management, Financial Management, Financial Risk Analysis and Management.
• Teaching Assistant: Database Systems (supported 200+ students, revamped lab sheets).

Work Experience:

• Software Development Intern at a prominent Indian fintech company: Optimized risk assessment micro-services, created a risk-based authentication system, and engineered a ticket deduplication system using Java.
• Software Development Intern at a fortune 10 company: Architected scalable data pipelines using Java, Scala, Apache Spark, and Apache Airflow, processed large volumes (we’re talking 100Tb+) of advertising data, and contributed to an Advertiser Insights Platform.

Projects:

• Graph-Aware LLM Tuning for Textual Node Classification: Implemented the ENGINE framework combining GNNs with LLMs, achieving state-of-the-art performance and significant reductions in training/inference time.
• Unix Shell : Developed a functional Unix-like shell in Java with command parsing, process control, I/O redirection, and piping.
• High-Speed Network Flow Analysis: Collaborated on a DRDO-commissioned project, enhancing ClickHouse tables and implementing TRIE encoding for JOIN queries, resulting in 98% performance improvement.
• University Fest Backend Infrastructure : Built a wallet system for a university fest, handling 24.9M INR in-app transactions, and managed 5000+ users with a micro-service structure.

Technical Skills:

• Languages: Javascript, Python, C++, Java, Scala
• Libraries & Frameworks: Django, Spring, Apache Spark, Apache Airflow
• Databases: PostgreSQL, MySQL, ClickHouse, BigQuery, DynamoDB
• Platforms & Other Software: Linux, Docker, Git, GCP, Jenkins, AWS, Loki, LockSmith

Achievements:

• NTSE Scholar
• Jee Advanced (AIR 2.5k)

I'm planning to take GRE by Sept 2025. I'd appreciate any feedback on how my profile stands for MFE programs, particularly regarding:

• What are the strongest aspects of my application?
• Where are the potential weaknesses or areas I should focus on improving?
• Should I keep MSDS as my backup option just because of tech experience?

Thank you in advance for your time and valuable input!

 

[Andy Nguyen]

Looks like a good profile for QD role. Your main research focus should be on programs and which one is a good fit for your career goal.
Every program has their own criteria and preference for candidates.
Look beyond the application stage to see how to become a strong job candidate. This will in turn make you a stronger candidate for MFE.

Thread 'MFE Recruiting Reality Check: What Every Incoming Student Needs to Know'

4/10/25

So you got into a MFE Program… Now What?​

There are plenty of articles on how to get into a MFE program… but how do you prepare for the intensely competitive internship recruiting that comes with a graduate program in quantitative finance?

I’m happy to share some insights from my three years of experience working in Career Services for Carnegie Mellon’s MSCF (Master of Science in Computational Finance) program.

The Reality of U.S. Recruitment
*Note: As majority of my students have landed internships and full-time roles in the US, I will focus on how to prepare for...

• Ash Cross
• Replies: 14
• Forum: Articles

• 
• 

 

[papashango]

Looks like a good profile for QD role. Your main research focus should be on programs and which one is a good fit for your career goal.
Every program has their own criteria and preference for candidates.
Look beyond the application stage to see how to become a strong job candidate. This will in turn make you a stronger candidate for MFE.

Thread 'MFE Recruiting Reality Check: What Every Incoming Student Needs to Know'

4/10/25

So you got into a MFE Program… Now What?​

There are plenty of articles on how to get into a MFE program… but how do you prepare for the intensely competitive internship recruiting that comes with a graduate program in quantitative finance?

I’m happy to share some insights from my three years of experience working in Career Services for Carnegie Mellon’s MSCF (Master of Science in Computational Finance) program.

The Reality of U.S. Recruitment
*Note: As majority of my students have landed internships and full-time roles in the US, I will focus on how to prepare for...

• Ash Cross
• Replies: 14
• Forum: Articles

• 
• 

My main goal is to get into MFE programs that are well-rounded, offering a strong blend of rigor, application, and even a bit of a research component. I genuinely believe I'd thrive in an environment where I can dive deeper into concepts and contribute to some extra research. Post-MFE, I'm primarily looking to pursue Quantitative Research (QR) or Quantitative Development (QD) roles.
Given this, what specific changes or improvements can I make to my profile to maximize my chances in such programs? I'm targeting to get into atleast 2 of the top 6-7 programs.

 

[Andy Nguyen]

One of the best things you can do to strengthen your application is to participate and immerse yourself in the activity that the programs offer to prospective applicants. There is frankly very little you can learn about each program just by looking at their website. The info there is often sparse and outdated.
You do learn a lot by joining their info sessions and come with plenty of questions. They offer this several times a year during admission cycle.
Then reach out to previous applicants on QuantNet. You can use the Tracker to find out exactly who applied to those programs and enrolled there. People are more likely to be frank with their review of the programs when they are a bit anonymous. It's a better environment to seek for review than via LinkedIn connections in my opinion. You can use LinkedIn to see how alumni of such programs progress 5 years post graduation and see if many have moved into your target roles.

By doing the above, you will have a much better idea of what they are looking for and whether a program is where you want to spend your money and launch your career from. You can customize your application based on the experience you learn from each program. Programs want to see applicants who spend time researching, not those that send in the same applications multiple places.