This document provides an overview of careers in quantitative finance from the perspective of Ashwin Rao, who has worked as a VP of quant strategies at Goldman Sachs and managing director of modeling at Morgan Stanley. It defines quantitative finance as roles requiring advanced math, stats, and computer science skills. Examples of roles include derivatives traders, strategists, modelers, and algorithmic trading quants. The document offers advice for students, such as developing coding skills, and warns that much learning will happen on the job. It also describes evaluating candidates' abilities during interviews and the company ZLemma's tools for algorithmic career guidance.

1. Careers in Quantitative Finance
A Student's Perspective
Stanford FinMath, May 2013
Ashwin Rao

2. My Background
● B.Tech. Computer Science. IIT-Bombay.
● Ph.D. Algorithmic Algebra. USC, Los Angeles.
● VP. Quant Strategist. Goldman Sachs, NY.
● Managing Director. Modeling. Morgan Stanley.
● Founder. ZLemma.com (A Tech Startup).

3. Define Quantitative Finance?
● For this talk, we limit the scope of definition to:
● Roles at Large Banks & Hedge Funds
● Trading Businesses involving Quant Analysis
● Requires advanced skills in Math/Stats/CompSci
● Requires sound understanding of trading markets

4. Some Examples
● Derivatives Trader
● Trading Desk Quant Strategist
● Derivatives Modeler, Econometric Modeler
● Algorithmic Trading Quant
● Analytics Developer

5. Trading Desk Strategist
● Focused on a specific business or product
● Deep knowledge of the specific market
● Blend of Math, Stats and programming skills
● Trading Strategies & Risk Management
● Work closely with Traders, Sales, Risk, IT, Ops

6. Derivatives Modeler
● Modeling stochastic dynamics of markets
● Solving derivatives pricing and hedging problems
● Expertise in Arbitrage-Free Pricing Theory
● Stochastic Calculus, PDEs, Numerical Methods
● Requires programming skills too, typically C++

7. Analytics Developer
● Requires strong Computer Science background
● Understanding of products and pricing models
● Tools for pricing, risk metrics, scenario analysis
● Data models, algorithms, functional programming
● Development of Domain Specific Languages

8. Algorithmic Trading Quant
● Markets are going increasingly electronic
● Systematic exploitation of market inefficiencies
● Analysis of historical market behavior & patterns
● Fleeting inefficiencies - Speed of execution key
● Systems programming & Statistics backgrounds

9. Preparation while at School
● Develop coding skills, eg: Python, Java, C++
● Algorithms, Databases, Numerical Methods
● Data Analysis, Econometric Modeling skills
● Avoid studying advanced quant finance
● Much of your learning will happen on the job

10. Theory versus Practice - Case Study
● Mortgage products are complex and messy
● Blend of risk-neutral and econometric modeling
● Understanding the 'Price of Model Risk'
● Capturing liquidity risk and transaction costs
● Need for advanced data/software enginering

11. Current Wall Street Scenario
● Regulations have hurt the industry
● Compensation levels down from 5 years ago
● People with STEM backgrounds are thriving
● Markets getting increasingly electronic
● More emphasis on vanilla trading businesses

12. What to expect during interviews
● Represent your abilities clearly and accurately
● Typically, a large and diverse set of interviewers
● Flood of puzzles, programming & math problems
● Questions in your claimed areas of expertise
● Evaluation of your communication and attitude

13. ZLemma - Algorithmic Career Guidance
● ZLemma.com evaluates your profile in detail
● ZLemma Quotient (ZQ) - your suitability for a job
● ZQ is your score out of 100 for a specific job
● Apply for high-ZQ jobs of interest to you
● Jobs ranging from Wall Street to Silicon Valley

14. Addendum
● Tune in to: blog.zlemma.com
● Write to: ashwin@zlemma.com
● Our app is your friend: zlemma.com