20 tasks, each one witnessed by the sources that watched the job — and behind every one, a prompt you can use tonight.
You usually split time between building models and talking to traders or researchers. Mornings often start with checking overnight market moves, running risk reports, and confirming that automated systems ran correctly.
The rest of the day is coding (C++ or Python on Linux), backtesting strategies, fixing bugs in models, and writing results into Excel or Power BI dashboards. Expect meetings to define model specs, data needs, and to review stress-test or scenario-analysis results.
Start with Excel and SQL-style data handling; you’ll use Microsoft Excel every day for quick analysis and building prototypes. Learn basic scripting (Python or shell) for automating tasks on Linux next.
C++ is required at many shops for production trading code. Add Microsoft Power BI for dashboards and Microsoft Azure or other cloud basics if the firm uses cloud services. IBM SPSS is useful for formal statistical workflows but not always required.
Use ML for research and feature engineering, but keep models transparent: log inputs, model versions, and performance metrics. That way traders and risk managers can review why a model makes decisions.
Never deploy a black-box model without backtests and scenario analysis showing behavior under stress. Store code and data in version control, run stress tests, and have human sign-off before production. Regulators and internal risk teams will expect that documentation.
According to the U.S. Bureau of Labor Statistics (BLS), there were about 132,130 employed in related roles and the median annual wage was $81,100. The lowest 10% earned about $48,460, and the top 10% earned about $151,490 per year.
Pay varies by city, industry, and experience. Junior roles or nonfinancial companies often sit near the median; prop trading or hedge funds push toward the top tenth. Use BLS data as a baseline and check job postings for local specifics.
Focus on linear algebra, probability, and statistical inference — enough to implement regressions and time-series models. Practice by coding: re-create standard models (OLS, ARIMA, GARCH) and test them on real market data.
Use IBM SPSS or Python libraries to run experiments, then put results into Excel or Power BI for reporting. Also learn how to design and evaluate stress tests and scenario analyses; those are daily tasks in production.
They overlap but differ in domain and constraints. Quants focus on finance: pricing, risk, trading metrics, and regulatory tests, often using C++ and running on Linux for speed. Data scientists may work across marketing, ops, or product, and use broader ML pipelines.
Quants must link models to wallets and P&L, interpret financial statements, and run stress tests. They also build trading metrics and sometimes ESG or carbon-pricing analysis — concrete financial impacts that data scientists don’t always handle.
The ability to take a business question and turn it into a concrete model or metric. That means asking what data you need, choosing a simple statistical method, implementing it (Excel, Python, or C++), and validating it with backtests or stress scenarios.
This skill ties coding, finance knowledge (like reading financial statements), and communication together. If you can deliver a clear metric or model and explain its limits, you become useful in weeks rather than months.