A Degree Designed for Your Career Destination
The career horizons for MFE graduates are broad and continue to broaden. The Berkeley Haas program prepares you to move forward in the direction that suits you best. From derivative traders to data scientists, quantitative research analysts to risk managers—our graduates are doing it all.
Premier investment and commercial banks, financial regulators, and stock market exchanges rely on Berkeley MFE grads as risk managers, traders, bankers, designers of specialized securities, and more.
As our lives and the global economy become more data-driven, MFE grads are finding their skills in demand far beyond the financial industry. They are creating algorithms for machine learning and working as data scientists in enterprises large and small, wherever innovation is appreciated.
Located next door to San Francisco, the financial capital of the West Coast, and Silicon Valley, home to entrepreneurs of all sorts, there is no better place than Berkeley Haas to launch the next stage of your career.
Sample Career Paths
Apply Quant Skills to Understanding Value
Sales and trading are key functions for any investment bank or hedge fund, on both the buy side and the sell side. These positions require an implacable curiosity and can be thought of as the detectives of the finance world. High frequency and algorithmic traders, for example, use their quantitative skills to uncover and profit from small price discrepancies or to identify expected recurring relative price relationships.
On the sell-side, team members consider the applicability and value of specific esoteric derivative products to meet a client’s objectives. Trading activities involve market-making, with the goal of profiting on the bid-ask spread, taking positions only as inventory.
Drive Decisions with Data
We live in a forest of data. Data scientists are trained to see the individual trees in that forest, to recognize the patterns they form, and discover insights that drive decision making.
In finance, data scientists contribute to creating viable financial products, building financial models, and managing risk. Using machine learning, they can build algorithms to predict the probability of a loan default or extract insights from gigabytes of data.
But data scientists play an increasingly important role in a wide range of enterprises. They help companies solve puzzles in product development, business planning, sales, and customer acquisition. In the tech sector, they use statistical modeling to identify and forecast trends and directions.
Inform Investment, Pricing, and Risk Management Decisions
Quantitative analysts develop and implement the complex mathematical models that finance industry firms use to make decisions about investments, pricing, and risk management.
The job combines the abilities of a logician and speculator to find the right balance between reducing risk and generating profit. These analysts are responsible for generating research ideas, building datasets, and analyzing statistical data.
Build the Tools and Strategies to Drive Trading Decisions
Strategists create software tools and libraries, mathematical models, and trading strategies to drive trading decisions. They provide quantitative business analysis to develop financial products, trading strategies, and risk reduction methodologies.
Their goal is to improve traders’ understanding of risk and to identify market opportunities. Models may address securities already on the books or about to be bought or bid on.
People in strategy and modeling often acquire special knowledge and facility in equity, credit, or interest rate derivatives, mortgage-backed securities, or other asset classes. They write documentation and contribute to the model-certification process and work with traders, quantitative strategists, controllers, and IT staff, among others.
Analyze Data, Identify Opportunities
Being part of a portfolio management team puts you in a position to influence how client money is managed. Depending on the firm, clients may be insurance companies and pension funds or retail investors. In all cases, you will use quantitative models to analyze vast data sets to identify opportunities that can be applied to large groups of securities to improve performance.
Quants working in portfolio management may also be responsible for creating optimal execution models that predict the best ways to trade blocks of securities without creating major price movements. They may use mathematical models to locate and leverage price misalignments or create algorithms that allow traders to exploit market news quickly and efficiently.
Balance Risk and Return
Risk and return are basic finance and business concepts. Finding the right balance between the two is the objective of risk management. Risk managers identify, compute, assess, and prioritize credit, operational, strategic, and other risks.
Since the term “too big to fail” came into vogue after the 2008 financial crisis, risk managers are more in demand by financial firms and government regulators. Their roles have become important and complex. Banks must comply with regulations governing capital, liquidity, buffers, and funding restrictions. Risk managers enable banks to quantify the riskiness of loans and determine the probability of default. Hedge funds rely on risk managers to assess the risks traders and portfolio managers are taking on.
Typical responsibilities include reviewing trading models and setting position limits and sector exposures. They evaluate liquidity ratios. On the regulatory side, they develop bank stress tests and establish regulatory capital levels, as well as develop risk management policies and frameworks to ensure compliance.
Bring the Broad Market Perspective
Firms like Duff & Phelps, EY, KPMG, and PwC are essential in the financial sector for their ability to take a broader view of the market and provide valuations from an independent, third-party perspective.
Consultants in firms like these employ communication skills as well as quantitative skills as they help clients with financial reporting and tax advisory services. They work directly with clients to assist them with key strategic and operational decisions, which makes communication and presentation skills a key skill for these roles. These firms work with a wide variety of clients, which means broader exposure to the market, and the chance to develop experience in a wide variety of financial instruments and industries.
Power Automated Trading
This work is where a passion for technology, software development, and mathematics converge. A position as a quant developer immerses you in the design, development, testing, and deployment of trading system software solutions that power automated trading. In addition, quant devs work closely with quantitative researchers and analysts to define priorities and deliver the best software solution for each situation.
Typically proficient in one or more specialized coding languages—C++ and Python, for example—quant developers bridge the gap between software developers and quantitative analysts. Familiarity with disciplines such as machine learning, natural language processing, platform development, and networking is an advantage for quant devs as well.