Talk given to Berkeley Financial Engineering Masters students. Overview of careers in quantitative finance.

1. Careers in Quantitative Finance
A Student's Perspective
Berkeley MFE, April 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