Data scientists have the skills to model complex systems, work with messy data, and uncover hidden patterns.

Quant scientists do all of that, but with the added thrill (and pressure) of putting real money on the line.

In this episode, we sit down with Jason Strimpel, Founder of PyQuant News and Co-founder of Quant Science, to explore why data scientists are uniquely positioned to excel in algorithmic trading.

Whether you're a data scientist curious about finance, or simply interested in seeing your models have a more personal impact, this show offers a fresh perspective on how your skills can translate into the world of algorithmic trading.

What You'll Learn:

• How your Python, stats, and modeling skills transfer directly into the markets

• The mindset shifts required

• Why reproducibility, auditability, and backtesting discipline are the data scientist's secret weapon

• Common pitfalls when transitioning into quant roles, and how to avoid them

• The tools and workflows Jason recommends to get started fast

🤝 Follow Jason on LinkedIn!

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