This document provides an overview of careers in quantitative finance, including roles such as derivatives traders, trading desk quant strategists, derivatives modelers, algorithmic trading quants, and analytics developers. These roles require advanced skills in mathematics, statistics, computer science, and a strong understanding of trading markets. The document discusses the job responsibilities and qualifications for some of these roles and provides tips for preparing for and interviewing for quantitative finance jobs on Wall Street.

1. Careers in Quantitative Finance
Ashwin Rao
Columbia University, CS Lounge
April 2013

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
● Algorithms, Machine Learning, Functional Prog.
● Probability, Linear Algebra, Stats Modeling
● Basics of Derivatives Pricing (book by Shreve)
● Avoid studying advanced quant finance
● Much of your learning will happen on the job

10. 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

11. Current Wall Street Scenario
● Regulations have hurt the industry
● Compensation levels down from 5 years ago
● Still good for people with STEM backgrounds
● The tide is turning
● More emphasis on vanilla trading businesses

12. 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

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