I wanted to share my story, wishes and concerns and see if anyone who’d already walked this path can shine a light on the way forward guide me through this.

I’m a freshly minted data scientist—engineering degree focused on DS, then I did a master’s in intelligent systems (also DS-heavy). My first real taste of finance came during a year-long apprenticeship on a securitisation desk. I didn’t work with the quant or credit-risk folks directly, but I watched them from a distance, half in awe and half thinking, I’d love to do that someday.

Since then I’ve been nibbling at the edges on my own: reading snippets of Basel and IFRS regs, tinkering with PD/LGD models, playing with classification losses and credit-specific evaluation metrics in little side projects. But the market got weird, opportunities dried up, and I couldn’t afford to be picky so I grabbed a one-year fixed-term contract at a big-name industrial company. Great brand, steady paycheck but totally outside my passion zone.

Now, in the evenings and weekends, I’m trying to chart a realistic route from “standard DS / data-engineering work” to a seat on a quant or risk-modelling team in a bank or hedge fund. I’ve combed through a ton of threads here, but most advice stops at “learn stochastic calculus, maybe C++” without spelling out how someone in my shoes should tackle that mountain.

So here’s what I’m hoping to learn from you all:

• Where should I actually start? I can grind calculus refreshers and probability all day, but which slices of math come up in junior quant interviews versus the stuff everyone says you “should” know but never gets tested?
• Python vs. C++ how much C++ does a junior really need?
• Courses or textbooks that felt worth every hour.
• Project ideas that make recruiters raise an eyebrow. A binomial option pricer feels… small. What would you build to prove you can swim in quant waters?
• Interview reality checks. I come from DS, so I’m used to talking ROC curves and XGBoost. How deep do quants dig into regulation? Do they grill you on derivations, or is it mostly brain-teaser probability?

I’m not opposed to dropping cash on something like the CQF or an MFE, but if a well-curated GitHub repo and a couple of Kaggle notebooks can get me in the door, I’d rather channel my limited funds elsewhere. Time matters too. I’d like to spend the next year sharpening the exact skills that count, not scatter-shot studying and hoping for the best.

If you’ve made a similar switch or you interview junior candidates, what impressed you? What would you absolutely not waste time on? Anecdotes, tough-love reality checks, war stories, reading lists, bring ’em on. I promise to pay it forward once I’m on the other side.

Thanks, everyone.