ML
MoneyLion Inc.
stock NYSE

Operations Research/ML PhDs for quant finance

In my experience they’re usually categorized as a sub-branch of MLE. AI engineer sounds like a bit of a tacky, buzzwordy and nebulous job title. It definitely depends on the employer though. As ML progresses, the scope of MLE work will keep expanding and there’ll be more and more opportunities to leverage ML in such a way that the complex math is abstracted away from you by frameworks and libraries. Always good to grasp the fundamentals though imo, hard to properly understand the pros/cons of a model and do performance troubleshooting if you don’t have a high level understanding of what’s going on behind the scenes.

this is a great point although i typically see these labeled as AI engineers rather than ML engineers

Seeking Advice: HFT Roles for Physics PhD with FPGA/Low-Latency ML Experience

The integration of QNX as the foundational security element within a broader technology stack is a powerful value proposition. This means security is "baked in" from the ground up, rather than being an add-on.
**Stack Evolution:**
**Layer 1 (Edge/Device):** QNX RTOS as the secure and reliable foundation for intelligent edge devices (vehicles, robots, medical equipment, industrial controllers). This includes secure boot, trusted execution environments, and real-time processing.
**Layer 2 (Edge Analytics/Middleware):** Solutions like BlackBerry IVY sitting on top of QNX, extracting, normalizing, and analyzing data at the edge. This layer also incorporates AI/ML models for localized decision-making and anomaly detection.
**Layer 3 (Connectivity & Secure Communications):** BB's secure communication protocols and platforms ensuring encrypted and authenticated data transfer from edge to cloud. This includes 5G integration.
**Layer 4 (Cloud Integration & Management):** Seamless and secure integration with hyperscaler cloud platforms for large-scale data storage, advanced analytics, and centralized device management (e.g., using BB's UEM for managing fleets of connected IoT devices).
**Layer 5 (Cybersecurity & Compliance):** BB's comprehensive cybersecurity portfolio (Cylance, enterprise services) providing continuous threat monitoring, incident response, and compliance adherence across the entire stack, from device to cloud.

I'm a GCP Big Data Engineer with 3+ years of experience (Python, PySpark, SQL, Airflow) looking to transition into a Quant role (sell-side, buy-side, prop trading). I've been self-studying finance, probability, stats, and linear algebra.
My strengths: Python, data manipulation, cloud (GCP), orchestration, problem-solving.
Seeking advice on:
* Skills Gap: What specific quant skills (advanced stats, ML for finance, C++, financial domain knowledge) do I need?
* Learning Resources: Courses
* Project Ideas: What kind of personal projects impress quant recruiters?
* Entry Points: Networking tips, potential stepping stone roles (e.g., Quant Developer)?
* Realism: Timeline and common pitfalls?
Any guidance from those in quant or who've made a similar leap would be hugely appreciated!
Thanks

I ended up in software engineering. Other roles might be ML/AI engineer or data scientist (though these are both mostly CS). I rarely use the double major in math, but I don’t necessarily regret it. I think the CS major opened up the most doors for me, but the math major does (slightly) boost my resume and it was fun.

Just open a brokerage account, we use Merrill lynch ML.com Merrill self directed account is free, you will connect your bank acct .. transfer money from bank to Merrill and buy what you want.

I see this often with ML/AI CV’s too, so I’m going to go ahead and ask: why add projects such as the portfolio optimisation one?
It’s so extremely generic and there are python libraries that can allow you to do this in minutes. You didn’t create anything, you just used a pre-existing framework to do something basic. I am based out of Europe so perhaps it’s more common to add generic projects to your CV, but to me it almost looks bad that you list such a thing on your CV.

BB Ibd im M&A könnte m.E.n. schon über 100k Einstiegsgehalt gehen (mit Bonus). Vermutlich eher in London realisierbar. Sonst Quants oder Pod trader. Ja, viele haben PhDs oder zumindest Masterabschluesse, ein Kumpel von mir hat es aber auch nur mit einem Bachelor zu Optiver geschafft (ueber 250k Einstiegsgehalt in Amsterdam). Definitiv aber auch ein Ausnahmetalent, v.A. Im ML und quantitative Finance Bereich.

People had crazy justifications for buying Intel in this sub. Things like “it has a small PE ratio” or “it dominates the notebook market” or “it has a lot of data centers!” It had lots of “cash on its balance sheet” and “it’s being turned around!”
Unfortunately some of these facts are out of date. They may have been true a decade or two ago but not now. They’re bleeding earnings actually. They also aren’t in the “cool” computing business which is GPU design. They only make CPUs which is not what foundational AI models use to train (or infer) ML experiments.
Maybe they will turn around but they can lose substantially more value before that occurs tbh.

It’s not just about being “wrong” but you should be asking about opportunity costs. If you bought sand held Alibaba you’d have paid enormous opportunity costs. It was down for ages, and in that time you would’ve missed out on a lot of nice gains on other stocks even though you’d eventually be in the green if you kept buying.
This sub is more so guilty of refusing to buy excellent businesses at fair prices. Nvidia at $100 would have been a life changing event for many of you instead of holding Intel at that time. Nobody has an answer to CUDA, and it’s already entrenched in ML applications. Developers love using it over ROCm or other solutions.
Other horrible picks in this sub would probably be NYCB (New York community Bancorp), Intel (x86 is peak mediocrity in the semiconductor industry now), Nike (death by competition), SNOW (bullshit ass grifting business that doesn’t have a niche), Lemonade, and others.

TL;DR:
* **The embedded software moat is deeper than it appears.** QNX powers 255 million vehicles and sits in all top 10 automotive OEMs because replacing safety-certified embedded software requires years of recertification. Competitors can't just code their way into this market.
* **They're riding the software-defined vehicle wave at the perfect time.** As cars become computers on wheels, QNX captures more value per vehicle through its new platform approach. The 55% pipeline growth in General Embedded Market shows the same tech works beyond automotive.
* **The Cylance divestiture was addition by subtraction.** Dumping the money-losing endpoint security business while keeping the AI/ML patents and tax losses turned BlackBerry from cash burner to cash generator overnight. They kept the valuable IP without the operational headaches.
* **Government relationships create sticky recurring revenue.** AtHoc's FedRAMP High authorization makes it the only critical event management platform cleared for the highest level of federal data. Near 100% renewal rates show these aren't just contracts but mission-critical dependencies.
* **The transformation is showing up in real numbers.** Going from $41 million net loss to $1.9 million profit in a year while beating guidance across both divisions proves the restructuring worked. They've removed over $150 million in annual costs while growing the profitable segments.
[https://beyondspx.com/article/blackberry-s-strategic-pivot-qnx-momentum-and-profitability-drive-the-narrative-bb](https://beyondspx.com/article/blackberry-s-strategic-pivot-qnx-momentum-and-profitability-drive-the-narrative-bb)

The math will be super nice for grad school should you ever choose that path (ML/AI/Bio/Stats/etc.). CS qualifies you for SWE/Data/other applied roles out of undergrad

Data Sci and MLE both heavily rely on math.
For MLE, math helps you to understanding the algorithms, diagnose issues etc. I’m a PhD Applied Math / MS CS student (BS Applied Math) and I guarantee I can run circles around most PhD CS students in diagnosing and fixing ML models simply by virtue of having a stronger understanding of linear algebra, prob/stats, and calculus than they do. Of course there are marginal returns on level of math, and a PhD (or even a masters) in Math isn’t necessarily useful for ML, but at the bachelors level its a huge leg up.
For data science, statistics is highly important, in fact it is probably the most important thing you will use. In DS, coding is only a tool you use to get a job done. Same with SWE — math builds far better problem solving skills than CS.

Merrill lynch self directed is free .. ML.com

Well I'm just an overly excitable regard and I thought you said what I wanted to hear. Anduril is just going to be free money because they're the armory for Democracy, and Databricks is going to own data preprocessing for ML and Dolly based systems are actually usable on a desktop and can scale for enterprise in a way other LLMs cannot.

I've really begun taking a liking to pricing theory, and all the emerging topics alongside it (rough vol, ML, optimal transport) but when I search up 'pricing quant internships' there aren't many roles specifically titled as a pricing quant, instead quant analyst / researchers.
I understand that majority of work at banks is to do with back office work like working on and maintaining pricing engines and not taking directional bets, so if I were to apply to quant analyst / researchers roles, can I assume I'll be matched to a pricing quant desk?
Also, I'm not too bothered by the comp as long as I enjoy my job but are pricing quants paid similar to risk quants? I'd imagine the former are paid higher because their models are directly used by traders

Hi everyone,
I’m a data scientist with 2 years of experience in the insurance industry, but my real passion lies in trading—especially algorithmic and quantitative approaches.
I’ve been building personal projects around this, like a Forex trend prediction model using LLMs (large language models) that forecasts N future candles based on historical data. I’m constantly experimenting with applying ML/AI to market data and want to take this more seriously.
I also enjoy working on custom tools—thinking of building a PyQt6-based screener/dashboard for real-time analysis and signal generation.
Do you think it’s possible to break into quant roles with a background like mine? What projects would you recommend that could demonstrate quant-style thinking or modeling depth?
Really appreciate any guidance, ideas, or resources!

I agree that we may be entering a new tech super cycle. The AI/ML boom reminds me of 2010-2014ish when companies like Uber/Lyft/Twitter etc were born. That could be a huge headwind for the economy.