The world of quantitative finance can feel enigmatic, yet it’s a rapidly growing field that combines math, technology, and finance to make significant impacts on the markets. To demystify this career path, MCS recently hosted a career exploration panel with alumni professionals in quantitative finance. The event, “Quantifying Quants: Exploring Fintech and Financial Tech Career Pathways,” brought together Raahul Acharya ‘20 (Quantitative Trader at Citadel), Eddie Tu ’22 (Quantitative Researcher at Two Sigma), and Nicholas Lopez ‘25 (Quantitative Trading Intern at Optiver). Here are some key takeaways to help undergraduates considering a career in this space.

Paths into Quantitative Finance: From College to Real-World Trading

Raahul Acharya, a CS concentrator and stats minor, has worked at Citadel for four years. Initially a quantitative trader, he has shifted to research, taking on a more developer-focused role as he builds and tests models that inform trading decisions. Raahul is currently on “garden leave” from Citadel—a mandatory period before moving to a competitor in the field. He is looking to join a smaller firm with a role more focused on building alpha signals.

Nicholas Lopez, a senior concentrating in Applied Mathematics and Computer Science, spent two summers interning at Optiver. His experience ranged from building tools and research projects to manually trading. Nick recently accepted a full-time offer as a Quant Trader at Optiver’s Chicago office, a role he’s excited to step into after graduation.

Eddie Tu transitioned from internships in machine learning (ML) at Amazon and financial markets research at MIT into quantitative research at Two Sigma. His current work focuses on systematic trading—using ML models to inform trading decisions. Eddie’s journey highlights that prior quant trading internships are not mandatory; he recruited without direct quant experience and also pursued roles in software engineering at big tech firms.

Key Quantitative Roles: Trader, Researcher, and Developer

Quantitative finance careers generally fall into three categories: traders, researchers, and developers. Here’s a breakdown of these roles and the responsibilities involved:

• Trader: Quantitative traders at firms like Optiver often have hands-on control, manually entering the market and making real-time trading decisions. This requires a keen understanding of the market and quick decision-making skills.
• Researcher: Quant researchers like Eddie work on building ML models that power systematic trading decisions. This often involves developing “alpha signals” that use data to predict long-term trade opportunities.
• Developer: Quant developers create the systems that execute trades. This role is critical in automated trading firms, where robust, reliable code is essential to success.

The distinctions between these roles can be nuanced, and panelists agreed that there’s often some overlap, particularly between trading and research.

Academic Preparation: Building a Strong Quant Foundation

For students interested in exploring the quant world, the panelists emphasized the importance of certain courses. Statistics 110 was universally recommended as a “must-take” course, along with linear algebra, which Raahul specifically highlighted. Additional recommended courses include:

• Stats 111 and 123 for in-depth statistical modeling,
• CS 126 and CS 181 for computer science fundamentals,
• Advanced 200-level courses, though not required, can also serve as talking points in interviews.

Research experience, especially in data-driven fields, is highly beneficial. For quant research roles in particular, Eddie pointed out that research skills translate well into the open-ended problem-solving needed in the quant world. Nicholas, who interned in data science at Capital One before joining Optiver, encouraged students to consider data science or analytics roles as they build transferable skills.

Navigating the Quant Recruiting Process

Unlike fields like investment banking, networking is less central in quant finance recruiting. Instead, preparation is all about technical and mental agility.  However, because each role and firm is unique, it is helpful to talk to people doing the work you are considering to make sure you understand those differences. Interview preparation commonly involves:

• Practicing with resources like The Green Book and Quant Guide.
• Mental math and speed drills to sharpen quick decision-making.
• Coding problems on LeetCode, especially for firms focused on systematic trading, as Eddie mentioned for his experience at Two Sigma.

Panelists advised that it’s essential to be clear and direct in interviews. In addition, Raahul suggested asking thoughtful questions about each firm’s trading style and team structure to better understand day-to-day responsibilities and long-term opportunities.

Career Paths and Future Opportunities in Quant Finance

Quant finance doesn’t have a one-size-fits-all career path. Panelists discussed how roles can vary between firms, even within quant trading. Here are some insights they shared:

• Systematic vs. Manual Firms: Firms like Citadel and Two Sigma focus on systematic trading, relying heavily on algorithms and automated decisions. Trading firms like Optiver use strategies that can give traders more hands-on control, adapting strategies in real time.
• Large vs. Mid-Size Firms: Raahul suggested that students consider smaller or mid-sized firms early in their careers, as these firms may offer broader exposure to various aspects of trading. Larger firms like Citadel tend to be more specialized, which can limit flexibility later.
• Concurrent Master’s Programs: Panelists generally agreed that concurrent master’s programs aren’t essential for breaking into quant finance directly after undergrad. However, Eddie noted that a graduate degree might give candidates a slight edge at firms like Two Sigma or D.E. Shaw.

For those still exploring options, data science, software engineering, and research internships provide a solid foundation for entering quant finance later.

Takeaways for Aspiring Quants

• Coursework: Prioritize core statistics and linear algebra courses, and seek opportunities to apply data science or ML skills in research settings.
• Interview Prep: Focus on technical preparation through quant-specific resources and practice coding if aiming for systematic firms.
• Firm Exploration: Understand each firm’s style—whether systematic or manual—and consider how well it aligns with your interests and career goals.

With these insights, students at Harvard and beyond can feel more confident in navigating the complex and rewarding world of quantitative finance. The field offers diverse roles where passion for math, problem-solving, and data can converge in impactful ways. As Raahul, Eddie, and Nick illustrated, there’s no single path to becoming a quant—it’s about building skills, asking questions, and finding the right fit for your career.