u/InternationalSet7503

So i have been on hold since past 1 week after being selected by HSBC on campus 2027, anyone else facing the similar situation or has been selected after being on hold or been rejected.. Please let me know your views and Timelines as to how much more time to expect for a concrete answer

Hey everyone,

I’ve been working on an F1 race prediction system since mid-2025 and wanted to share the technical approach for feedback.

The system predicts race finishing order using:

• FastF1 historical race data
• starting grid position
• driver and team recent form
• circuit-specific features such as safety car probability, pit loss, overtaking index, long-straight index, braking intensity, degradation, and weather risk
• Random Forest regression
• Monte Carlo simulation for podium and top-10 probabilities
• FastAPI backend
• Next.js dashboard for visualization

The model predicts finish_pos - grid_pos, then reconstructs the expected finishing order from the starting grid. I also use the spread across individual trees to estimate uncertainty and generate prediction intervals.

So far this season, the system has predicted the race winner correctly in 6/6 races, but I’m aware that winner prediction alone is not enough to judge model quality. I’m also tracking feature importance, predicted rank error, podium probability, and top-10 probability.

Some limitations I’m trying to improve:

• tire strategy is still simplified
• DNFs and safety cars are not fully simulated
• Monte Carlo currently assumes mostly independent driver outcomes
• driver/team priors still need manual updating
• qualifying position is still very dominant in the model

I’d really appreciate feedback from people interested in F1 analytics, machine learning, simulation, or race strategy.

What would you improve first: tire modeling, correlated Monte Carlo, FP1/FP2 long-run pace, or automatic current-form updates?

# Help Me Decide: Should I Do MS in USA, Canada, or Europe? (Data Science/AI focus)

**Context:** Indian CS student (8.23 CGPA, NIT Goa), interested in ML systems and AI. Built a couple production projects (RAG system, F1 predictor, web app). Currently at IIT Madras internship. Thinking about MS in Data Science or CS with ML focus.

**Dilemma:**

- **USA (Stanford, CMU, Berkeley, UT Austin):** Best prestige and salary, but visa is risky and tuition is expensive ($80–100k/year).

- **Canada (Toronto, Waterloo, UBC):** Easier visa + residency, good job market, reasonable cost, but smaller market than US.

- **UK (Imperial, Edinburgh, Oxford):** Strong research, but expensive, visa uncertainty post-Brexit, and only 1-year programs.

- **Germany (TU Munich, TU Delft):** Cheap ($10–30k/year), easy work visa, but slightly lower job market and salary.

**Questions:**

1. If I want long-term stability + good job prospects, is Canada the clear winner?

2. If I want prestige + highest salary, should I go US despite visa risk?

3. Is my profile (8.23 CGPA, NIT Goa) competitive for top-20 schools?

4. Should I take GRE?

What would *you* choose? Would love practical advice from people who've been through this.

Quick background: 4th year student, NIT Goa, 8.2 CGPA (India). Built production ML systems (RAG with provenance, F1 prediction with uncertainty, full-stack hostel portal). Interested in trustworthy ML, environmental analytics, and F1 forecasting. Planning to apply to MS programs (USA/Canada/UK/Germany) this fall.

Key questions:

1. Is an 8.23 CGPA from a tier-1 Indian institution competitive for top-20 programs (CMU, Berkeley, Toronto, Imperial)?

2. For F1 analytics interest: better to do Data Science MSc + F1 projects on the side (flexible), or target Motorsport Engineering MSc (direct pipeline)?

3. Program recommendations: Toronto vs. Imperial vs. Berkeley vs. TU Delft vs. Edinburgh? Trade-offs on visa, cost, research, job market?

4. For the F1 predictor: I've achieved "6/6 winners correct" on 2026 races, but sample size is tiny. Should I report this, validate it rigorously (Brier score, calibration), or downplay the claim?

5. Should I focus on trustworthy ML / uncertainty quantification as my core thesis, or be more domain-focused (environmental, sports)?

Open to honest feedback and reality checks. What am I missing?

Help Me Choose: MS in Data Science / AI / CS – Profile, Programs, Countries

TL;DR: 8.23 CGPA from NIT Goa (India), strong projects (RAG system, F1 prediction, hostel portal), interested in trustworthy ML for high-stakes domains (environment, infrastructure, F1 analytics). Deciding between USA, Canada, UK, Germany for MS. Should I aim for top-20 (MIT/CMU/Stanford level) or play it safe? Is F1 path viable through Data Science vs. Motorsports MSc?

MY PROFILE

Education: B.Tech ECE with CS minor, NIT Goa, expected 2027 (moving into 4th year) CGPA: 8.23 / 10 Current: Summer internship at IIT Madras (June–July 2025) Experience: 2-month railway internship (South Western Railways) learning safety-critical systems

PROJECTS (STRONG POINTS I HOPE)

1. JITK – RAG-style policy retrieval system with one-click PDF provenance (FastAPI, pgvector, Sentence Transformers, Next.js). End-to-end ML pipeline with citation grounding, confidence-based abstention, latency metrics.
2. F1 Race Predictor – Python/scikit-learn, data from 2023–2025. Random Forest with leakage-aware rolling form features, circuit context, uncertainty modeling via prediction intervals + Monte Carlo. Claim: 6/6 winners correct this year (I know this is sus; need to validate properly).
3. HostelMitra – Full-stack hostel complaint portal (Firebase, Firestore, Google OAuth). Real-time tracking, role-based controls, audit trail. Actually used by NIT Goa students.

SKILLS & CERTS

Python, SQL, C/C++; pandas, NumPy, scikit-learn, TensorFlow/Keras Andrew Ng Machine Learning + Deep Learning Specializations Ranked 251/7000+ in Amazon ML Challenge 2025 National case study finalist, India Innovates finalist

INTERESTS

Primary: Trustworthy ML, ML systems, uncertainty quantification Secondary: Environmental / pollution ML, F1 / motorsport analytics, real-world ML deployment Flexible: Open to most domains if the ML is rigorous and high-stakes

MY QUESTIONS

1. How competitive am I realistically? Is 8.2 CGPA from NIT Goa enough for top-20 programs (MIT, CMU, Oxford, Cambridge)? What about top-30?
2. Country choice: Should I prioritize USA (prestige + salary) vs. Canada (visa stability) vs. UK (research + F1) vs. Germany (cost + stability)? What's the ROI in 2025–2026?
3. F1 Analytics Path: Should I do a pure Data Science / AI MSc and build F1 projects on the side (more flexible) or target a Motorsport Engineering MSc (direct pipeline but risky if F1 doesn't hire)? Thoughts on Cranfield, Oxford Brookes, Southampton?
4. Program Choice: Any specific program recommendations? Toronto, Imperial, Berkeley, Waterloo, TU Delft, Edinburgh, TU Munich?
5. F1 Predictor Validation: How do I validate my "6/6 winners" claim properly? What metrics should I report (Brier score, calibration, baselines)?
6. Timeline: GRE by August 2025? Any other exams needed?

Any advice / harsh truths / reality checks welcome. I'd rather hear criticism now than find out after applying.

Thanks!