Talking Tuesdays with Fancy Quant

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What does it really take to become a mathematician, a quantitative researcher, or a successful data scientist in the age of AI?

In this episode, I sit down with Devashi, a Quant Model Developer at Agora Data and a mathematics PhD specializing in algebraic topology, to explore the fascinating journey from studying mathematics and computer science in India to pursuing advanced mathematical research and working in quantitative finance.

We dive into why learning mathematics requires more than memorizing formulas, why struggling through difficult problems is essential to developing real expertise, and what AI might be getting wrong about the process of discovery.

In this conversation, we explore:

- From India to quantitative finance: Devashi's educational journey from BITS Pilani, Goa, to a mathematics PhD at the University of Georgia and a career in fintech.
- Why study topology? How abstract mathematics changes the way you think about shapes, distance, equivalence, and complex problems.
- Pure math vs. applied math: Why mathematicians sometimes pursue questions out of curiosity rather than immediate practical applications—and how those discoveries can eventually become useful tools.
- AI and the future of education: Why getting an answer from ChatGPT isn't the same as understanding the problem, validating the solution, or learning the underlying concepts.
- The value of struggling: Why making mistakes, working through difficult textbooks, and learning from other people are critical to developing genuine expertise.
- Mathematics and quantitative finance: How abstract mathematical thinking can provide new perspectives on risk management, modeling, and problem-solving.
- Rethinking how we learn: Why older textbooks, curiosity, collaboration, and a willingness to ask “why?” can make a difference.
- Building a career after a PhD: How having a backup plan, networking with genuine curiosity, and developing practical skills can help mathematicians transition into industry.

One of the biggest themes of this discussion is that learning is a process, not simply the production of an answer. Whether you're studying mathematics, developing financial models, building software, or using AI, understanding the assumptions, reasoning, and limitations behind a solution still matters.

AI can be a powerful tool, but what happens when we stop doing the hard work of learning how to think?

Watch the full conversation and join the discussion in the comments: Is AI making us better problem-solvers, or are we losing the ability to solve problems ourselves?

If you're interested in quantitative finance, mathematics, data science, AI, risk management, and the future of education, subscribe for more conversations exploring the ideas behind the numbers.

#ArtificialIntelligence #Mathematics #QuantitativeFinance #DataScience #Topology #AIinEducation #QuantFinance

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What’s next for quantitative finance education? In this episode of Talking Tuesdays with Fancy Quant, we sit down with the Director of Carnegie Mellon University’s Master of Science in Computational Finance (MSCF) program to discuss how quant finance is evolving, new educational opportunities, and what students need to know to prepare for the industry.

From the growing demand for quantitative talent in commodities and energy markets to new research initiatives, AI, deep learning, and the future of finance in Miami, this conversation explores how one of the leading quantitative finance programs is adapting to a rapidly changing industry.

In this episode, we discuss:

The Commodities Quant Lab: Carnegie Mellon’s new initiative focused on quantitative research in commodities and energy markets, connecting graduate students, PhDs, and industry professionals.

The Growing Role of Energy and Commodities: Why commodities are becoming an increasingly important area for quantitative finance and where new career opportunities may emerge.

New MSCF Curriculum: Updates involving deep learning, blockchain, cryptocurrency, and the evolving technical skills needed by quantitative finance professionals.

Experiential Learning and Recruiting: How trading competitions, market-making games, poker events, and industry partnerships help students connect with banks and proprietary trading firms.

Carnegie Mellon’s Miami Campus: What the university’s planned expansion could mean for research, finance, technology, and the broader quantitative ecosystem.

Career Development: How specialized career coaching and business communication training can help students prepare for interviews and succeed in competitive recruiting environments.

AI and the Hiring Process: The challenges of recruiting in a world of tens of thousands of applications, automation, and potential AI-driven interviews.

Choosing the Right Graduate Degree: When a specialized master’s in financial engineering makes sense—and when statistics, computer science, or a PhD might provide greater flexibility.

We also explore the potential connections between quantitative finance, energy markets, Florida’s growing financial ecosystem, and opportunities across the United States and Latin America.

Is a master’s in quantitative finance worth it? The answer depends on your career goals, your understanding of the industry, and how well you prepare for the realities of the job market. This discussion offers insight into how students can make informed decisions and build skills that translate into real opportunities.

Whether you're considering an MFE or MS in Computational Finance, already working as a quant, exploring energy trading, or interested in the future of financial engineering, this conversation provides a look at where the industry and quant education may be heading.

Subscribe for more discussions on quantitative finance, financial engineering, AI, risk management, data science, machine learning, graduate programs, and careers in the quant industry.

#QuantFinance #CarnegieMellon #ComputationalFinance #FinancialEngineering #QuantCareers #EnergyTrading #MachineLearning #AIinFinance

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What happens when a former Bank of America quantitative engineer combines deep expertise in finance with cutting-edge AI? In this episode of Talking Tuesday, I sit down with Katelyn Schoenberger, founder of MarginLens, to discuss how she built an AI-powered margin intelligence platform from the ground up.

We dive into her journey from computer science student to quant developer at JP Morgan and Bank of America, and ultimately to becoming a solo founder building a fintech startup. Along the way, we explore AI-assisted software development, startup life, quantitative finance careers, and why understanding margin is far more important than most people realize.

Whether you're interested in quantitative finance, AI engineering, fintech startups, or breaking into Wall Street, this conversation is packed with practical insights and career advice.

In this episode:
Building an AI-powered margin platform as a solo founder
Life as a quantitative engineer at Bank of America
Margin explained: what it is and why it matters
How AI is changing financial infrastructure
Startup lessons from building a production-ready fintech product
Claude Code, AI-assisted software engineering, and modern development workflows
Breaking into quantitative finance without attending a target school
JP Morgan vs. Bank of America career experiences
Software engineering vs. quantitative finance careers
Why documentation, architecture, and explainability still matter in the AI era
Career advice for students and aspiring quants
Women in STEM and navigating technical careers
The importance of creativity, hobbies, and lifelong learning

If you're considering a career in quantitative finance, software engineering, machine learning, or fintech, this interview offers an honest look at what it takes to succeed—and how AI is reshaping the industry.

👍 If you enjoyed this interview, please Like, Subscribe, and leave a comment with your thoughts or questions for a future episode.

 #QuantFinance #ArtificialIntelligence #Fintech #MachineLearning #SoftwareEngineering #BankOfAmerica #JPMorgan #ClaudeCode #Startup #WallStreet #RiskManagement #Trading #DataScience #CareerAdvice #FancyQuant 

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A long form discussion on the history of programming for quants at the banks from SAS to Python and the rise of machine learning. While the hype of machine learning changed some of the banking modeling practices the industry kept their head straight as regulators closely watched. Now in 2026 with the AI hype wave and regulators being less strict, the banks seem lost as many are calling to replace SR 11-7 with general frameworks to reduce risk management and drive more efficiency. While this sounds good from a profit perspective, it is throwing out common definitions of models in a chase for non-sense.

Balancing the hype and trends also has been hard with the YouTube channel. It is much easier to sell snake oil as everyone wants a magical cure to profit, high salaries, minimal work, programming, math, and stats without putting in any work. I have done fairly well at sticking to creating real quant educational materials and industry perspectives even when passing up easy growth opportunities. Ten years later and we passed 100k subscribers without selling out.

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Didier Lopes studied control systems and electrical engineering in Europe before moving to the United States to build OpenBB, a next-generation financial research workspace.

Frustrated by how fragmented data, analysis, and decision-making tools were across finance and other industries, Didier set out to create a professional-grade platform where teams can bring their data, analytics, and AI into one unified environment.

Before founding OpenBB, he worked on cutting-edge technologies ranging from self-driving cars to wearable fitness devices, following a deep curiosity for complex systems and real-world impact.

In this episode, we talk about building in the age of AI, navigating life as a founder in the U.S., and the nuances of time-series modeling in financial markets.

OpenBB Examples:
https://openbb.co/solutions

OpenBB Product:
https://pro.openbb.co/

LinkedIn:
https://www.linkedin.com/in/didier-lopes/

High Growth Handbook (book recommendation - affiliate link):
https://amzn.to/4aAvbfS

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Jeffrey Rosenberg, CFA, Managing Director is a senior portfolio manager within BlackRock Systematic. He leads active and factor investments for mutual funds, institutional portfolios and ETFs within BlackRock’s Systematic Fixed Income (“SFI”) portfolio management team. In this role he serves as a member of the SFI Investment and Executive Committees and as a senior portfolio manager for several investment products including the BlackRock Systematic Multi-Strategy Fund (BIMBX), the iShares Systematic Alternatives Active ETF (IALT) and the iShares Managed Futures Active ETF (ISMF).

We talked about systematic portfolio managers compared to discretionary portfolio managers, his career and education from finance and math into computational finance at Carnegie Mellon, the changes in the market from banks to hedge funds being driven by the global financial crisis, and some book recommendations.

BlackRock Systematic Investing: https://www.blackrock.com/us/individual/investment-ideas/systematic-investing

BlackRock’s Q1 Fixed Income Outlook:
https://www.blackrock.com/us/financial-professionals/literature/market-commentary/fixed-income-market-outlook.pdf

Jeffrey Roseberg:
https://www.blackrock.com/us/individual/biographies/jeffrey-rosenberg

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Michael Jordan Pilgreen was a poetry and art major but as covid hit and the commercial art industry changed he looked for a new path. He considered financial planning but ended up learning finance and technology to become an engineer on Wall Street in the fixed income market. Now he is a co-founder of Enduring Markets which is a capital markets publication covering a range of interesting topics.

I got the privilege to advise Michael about 5 years ago during his journey on what quant finance was. Today I got to learn more in depth about fixed income from him.

Michael Jordan Pilgreen
https://www.linkedin.com/in/michael-jordan-pilgreen-7a3099108/details/experience/

Enduring Markets
https://www.enduringmarkets.org

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Interested in what quantum computing is? Ramis Movassagh explains the basics of quantum, some applications to finance, why it is a hard problem to solve, and ways to learn more about it.

Ramis is an applied mathematician and a theoretical physicist who researches quantum computation and information theory, and quantum cryptography and complexity. He does an amazing job at laying out some of the basic ideas of quantum mechanics.

Ramis' Links:

LinkedIn:
https://www.linkedin.com/in/ramis-movassagh-33465717/

Website and Blog:
https://ramismovassagh.wordpress.com/
https://ramismovassagh.wordpress.com/blog

X handle:
@Ramis_Movassagh

Quantum supremacy paper:
https://www.nature.com/articles/s41567-023-02131-2

PDF for those behind the paywall:
https://www.nature.com/articles/s41567-023-02131-2.epdf?sharing_token=oYgyql7M-nUPNwLJ4F2Q_tRgN0jAjWel9jnR3ZoTv0M5gli-apQIlZ1xThgS5KRp3t28rkad24bSeQ-gRMhmOaNP232U_FZZQjPrseDCTdXIRryTWL339snJllwZAjuD5PMkLKij96GMA_OniVnTz5JjaARH0qW5OV-AKwZr4VI%3D

Press coverage:
https://phys.org/news/2023-09-difficulty-simulating-random-quantum-circuits.html

https://communities.springernature.com/posts/the-quest-for-quantum-primacy

Quantum Merkle Trees:
https://quantum-journal.org/papers/q-2024-06-18-1380/

IBM's "Basics of Quantum Information":
https://quantum.cloud.ibm.com/learning/en/courses/basics-of-quantum-information

John Preskill from CalTech:
https://www.preskill.caltech.edu/

MIT "Quantum Computing":
https://ocw.mit.edu/courses/18-435j-quantum-computation-fall-2003/

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Diving into pure math, quantitative finance, and solving business problems with AI and LLMs. Cheikh Fall is a co-founder of Sabr Research and an MIT grad who is looking to solve many business problems with the advancements in AI. We discuss potential issues with AI, what hedge funds look for in quant researchers, and how to prepare as a student for AI.

Cheik Fall
https://www.linkedin.com/in/fallcheik/

Sabr Research
https://www.linkedin.com/company/sabr-research/


Dimitri Bianco
https://www.linkedin.com/in/dimitri-bianco/

My favorite coffee which supports the creation of these podcasts
https://volcanicacoffee.pxf.io/yq6Qj3

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I had a blast taking to Michael Watson who is the founder and CEO of Hedgineer. 

"AI is the future. But not everyone is prepared for it. To effectively integrate AI into a company, it needs an organized, thorough, and secure inventory of data. Without one, every AI implementation remains incomplete because it only has access to a subset of the relevant information it needs.

Most hedge funds are built on disjointed networks of research portals, order management systems, and datasets. The ones that don't consolidate them will run the risk of falling behind the AI curve."

Michael's LinkedIn:
https://www.linkedin.com/in/michaeldavidwatson/

Hedgineer:
https://www.hedgineer.io/
https://www.youtube.com/@hedgineer
https://podcasts.apple.com/us/podcast/the-hedgineer-podcast/id1674929284
https://open.spotify.com/show/0Aq6pxdUONA4aJPntsvqf2
https://www.linkedin.com/company/hedgineer-io/

Video Version:

https://youtu.be/2hyfMMl1pGg

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