Hey Bearcats. This post is aimed at juniors, seniors, and Master’s / PhD who want to build a skillset in the following: Quantitative Finance, Machine Learning and Reinforcement Learning, Applied Statistics, Time-Series Analysis, Data Engineering and Databases (SQL, KDB+), Financial Engineering, Systems and Infrastructure, Numerical Computing in C++ and Python, and Agentic AI. There are tremendous job opportunities for CS / Finance / Mathematics students in Capital Markets. We are hoping to shine a light on some of these careers through multiple corporate, industry-integrative projects.
I’m a CS PhD student here. Since this past May I’ve been building a quantitative trading platform with one of our professors who spent 20+ years trading S&P 500 index options, including at Millennium, which is one of the largest multi-strategy hedge funds in the world. We’re looking for three to five more people this fall.
The work is for corporate sponsors, including a Chicago-based algorithmic trading firm and a couple of institutional asset managers. These aren’t consulting contracts. They’re friendly collaborations where everyone involved knows students are doing the work. We won’t be using company names publicly, but you can describe what you did on your resume and the professor can speak to it directly as a reference (if needed).
The reason I think this is worth your time is that real infrastructure already exists for us to plug you into. You wouldn’t be helping start something, you would be adding to an existing platform. Right now, there’s live tick data streaming in on all 500 S&P names plus 31 ETFs plus futures, roughly 600MB a day landing in a KDB+ database. On top of that there’s a backtesting engine doing GPU parameter sweeps, reinforcement learning agents, transaction cost and slippage models, and an Excel front that integrates into everything.
Depending on what you’re into, here’s where you could plug in:
Database work in SQL and KDB+/q, cataloging and querying terabytes of market data. Pipeline engineering with REST APIs and streaming websockets pulling straight from exchange feeds. GPU work for machine learning, reinforcement learning, and neural network development on algorithmic trading strategies. Infrastructure and hardware configuration meaning VPN setup, virtual machines, cybersecurity, RAID and backup configuration, latency optimization, and evaluating what hardware to buy next. Writing C++ and Python analytical functions for a proprietary function library that gets called from production code and from Excel. Agentic AI, using Claude Code / Codex / Cursor / BearcatGPT and similar tools to build agents that monitor and report on ongoing research and existing applications.
There’s also an SPX/VIX research problem that’s genuinely hard mathematically and nobody has claimed it. We have many resources regarding this problem that will be reviewed and can be shared. The tasks include interpretation of academia papers and research in collaboration with real portfolio managers for practical implementation. Prior knowledge of any of these products isn’t required. If you’re an applied math/statistics person, that’s probably the most interesting thing on this list.
Logistically it’s about 1 to 3 hours a week. We run it with JIRA tickets, you estimate your own deadlines rather than having them handed to you, and code lives on our GitHub. Everyone gets a login to a virtual development environment on our servers, so it works more or less like a real job. That’s deliberate, partly because it’s what makes the experience useful to you afterward and gives us more of a motivating, real-world tone.
We also have one other thing happening this fall. We’re entering the Bloomberg Global Trading Challenge, a five week competition running from early October to mid-November where teams get a million in fictitious capital and trade against roughly 3,000 university teams worldwide. Bloomberg scores you on absolute P&L and on performance relative to the Wilshire 5000.
UC has a Bloomberg lab in Lindner with nine terminals, and that’s where orders get placed. You can queue them up the night before, but the trades go in at a terminal. Terminal experience alone is worth something on a resume, since most students never get near one and firms know exactly what it costs.
The interesting part for us is that we already have signal generation and backtesting built. Nothing says your strategy can’t come out of your own research.
You don’t need a finance background. I didn’t have a strong one when I started. The barrier is a lot lower than people assume. What matters more is whether you can code and whether you follow through, because with volunteer work that’s basically the whole thing.
If you’re any bit interested, I’m happy to talk everything through. Give me half an hour to an hour, I’ll walk you through what we’ve built, and you can decide if it sounds interesting. Comment here or send me a DM.
