Parallel Tracks: Working, Learning, and the Rhythm of Growth
Parallel Tracks: Working, Learning, and the Rhythm of Growth
Some people ask how I'm managing it—full-time at Mitsubishi HC Capital, full-time in the MEng program, gym at 9 PM, repeat. The truth is, I don't think about it much. I just show up.
Every day is the same loop: finish a credit analysis, drive to campus, study machine learning theory, lift weights, sleep, wake up, do it again.
For most people, I imagine that sounds like too much. But I love it. Not in the romantic, motivational-poster way—I love it the same way you love a rhythm once you've locked into it. The tempo becomes familiar. The grind becomes home.
September: Two Parallel Tracks
Since September, I've been running two simultaneous tracks: structuring deals in equipment finance by day, diving into computing and software by night. On paper, they don't overlap. In practice, they're teaching me the same lesson from opposite angles—systems thinking, pattern recognition, and the art of making decisions under incomplete information.
Finance
Computing
By day, I'm building financial spreads, pulling numbers from balance sheets, calculating EBITDA, running 4-blocker credit evaluations to determine if a client gets funded or not. The stakes are real—hundreds of thousands to millions daily, construction equipment, transportation fleets, deals that move or don't based on the numbers I put together in Excel and the analysis I present. It's precise work. Every cell matters. One miscalculation and the deal structure falls apart.
By night, I'm in CAS 735 with Dr. Mosser, learning that what I thought microservices were was completely wrong. Turns out, I'd been confusing modular architecture with actual service boundaries, misunderstanding how orchestration and choreography differ at scale. I'm glad to learn it properly now. Dr. Mosser has a way of breaking down distributed systems that makes the complexity feel manageable—like black-boxing a monolith one service at a time.
Then there's CAS 751 with Dr. Asoodeh—Information Theoretics in Machine Learning. His course is hard. Not in the "grind LeetCode for six hours" way, but in the "rethink everything you thought you understood about probability and entropy" way. He explains concepts intuitively, pulling analogies from old stories and dropping life advice in the middle of a proof. The kind of professor whose words stick with you long after the lecture ends. He's been accommodating too, understanding when the workload stacks up, which I'm grateful for.
What I'm learning in both worlds is starting to converge. Finance taught me how to model risk and quantify uncertainty. Machine learning is teaching me how to formalize that intuition mathematically. The gap between the two is smaller than I expected.
Applify AI: Passion to Product
Organic growth, real impact. Saving job seekers 35+ minutes per application.
Somewhere between the credit memos and the problem sets, Applify AI crossed 150 active paying users. I still remember building the first version late at night, frustrated with how long resume tailoring took for my SKompXcel students. Forty minutes per application, every single time. It was a problem I had, they had it, and thousands of job seekers were living it daily.
So I built a solution. What started as a side project—a tool to shave time off the resume grind—turned into something real. A system people pay for. A product that works.
The growth has been steady, organic. No massive marketing push, no viral moment. Just people using it, finding value, telling others. That's the kind of growth that matters. The support from users has been incredible—seeing people land interviews, save hours of their week, and actually feel confident about their applications. That's the payoff.
This is what I'm all about. Taking a problem, understanding it deeply, and building a system that solves it. Not just code for code's sake, but tools that change how people work.
Applify started as a passion project born out of necessity. Now it's a functioning product with real users, real revenue, and real impact.
Balancing Applify with work and school means late nights and weekends disappear into feature updates, bug fixes, and user support. But I wouldn't have it any other way. Turning passion into a working system is the entire point. That's the craft. That's the satisfaction.
2025: A Game Changer
My GitHub contribution chart is creeping up on 2,000 commits this year—far more than ever in my life. Between Applify updates, SKompXcel tools, and grad school projects, the pace hasn't let up.
The grid doesn't lie. 2025 has been about execution.
The People Who Made It Possible
None of this works without the people who took a chance on me.
Siraaj Grewal
Team Lead of Construction and Finance—gave me a shot in this space when I didn't have a BCom. He saw something worth investing in and brought me into the fold. I've learned more about credit, deal structuring, and client relationships in the past few months than I could have imagined. He didn't just give me a job—he gave me an education in a field I'm growing to love.
Elizabeth Wylie
One of my account managers—made onboarding seamless. She's organized, sharp, and has been absolutely killing her numbers recently. Watching her work has been a masterclass in efficiency and client management. She's welcoming in a way that makes the steep learning curve feel less intimidating.
Dr. Farmer & Dr. Paige
Agreed to supervise my master's. From the start, they've been supportive, writing reference letters for my application and guiding me through the early stages of defining what I want out of grad school. I'm looking forward to learning from both of them as the program progresses.
Dr. Sekerinski & Dr. Smith
Kindly wrote reference letters supporting my admission. Their endorsement meant more than they probably know, and I don't take that lightly.
The Routine: 6 AM to 10 PM
The schedule is consistent. Wake up, structure a deal, review credit applications, coordinate with legal and ops to close transactions cleanly. Salesforce updates, pipeline management, client calls. Around 5 PM, I leave the office and head to campus. Lectures, problem sets, study sessions. Around 9 PM, I hit the gym—compound lifts, nothing fancy, just enough to keep my head clear and my body sharp.
Then home. Sleep. Repeat.
Most people would probably find this exhausting. I find it clarifying. There's no time to overthink, no space for doubt to creep in. Just execution. The loop keeps me focused, keeps me moving forward.
I won't pretend it's easy. There are nights when the problem sets blur together, mornings when the credit memos feel endless. But I've learned that the hard things are usually the ones worth doing. The struggle isn't a sign you're on the wrong path—it's proof you're on one that matters.
Progress in Other Forms
Somewhere in the middle of this schedule, I hit 172 lbs. My goal was always 170—I achieved it while juggling all of this. Haven't been able to train martial arts because I've been so busy, but I've been grinding in the gym regardless. Now it's time to set one final goal: 175. After that, I'll get as lean as possible and maintain.
My younger sister is at McMaster now, first-year engineering. Helping her study and plan for some of the same concepts I tackled years ago is surprisingly rewarding. It reminds me of my early years in undergrad—when everything felt new and overwhelming, but also full of possibility. Watching her navigate the same labs, debug the same kinds of problems, and ask questions that sound familiar brings it all full circle. She's walking the same path I did, and getting to be part of that from the other side means more than I expected.
What's Next
I don't know exactly where this parallel-track experiment leads. Maybe I'll build systems that bridge finance and ML. Maybe I'll discover something entirely different along the way. For now, I'm content to keep running both tracks, learning as much as I can from each.
The Vision
Build intelligent systems that help people make better financial decisions, faster—bridging the gap between data and decision-making.
MEng Graduation: December 2026
The MEng program runs until December 2026. By then, I'll have spent over a year structuring deals, modeling amortization schedules, and learning how machines learn. The intersection of those two worlds is where I want to operate—building intelligent systems that help people make better financial decisions, faster.
But that's the future. Right now, it's 9 PM, and the gym is calling.
Time to go.
Alhumdulilah.