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12 Months On Campus Masters Program
University of California BerkeleyThe Master of Financial Engineering (MFE) at the Haas School of Business, University of California, Berkeley, is a premier, STEM-designated, one-year intensive program that prepares students for advanced roles in quantitative finance, data-driven investing, risk management, and financial technology. Known globally for its strength in financial modeling, machine learning, and computational finance, the MFE leverages Berkeley’s deep academic resources and close proximity to both Wall Street and Silicon Valley.
The program is ideal for students with strong backgrounds in math, statistics, computer science, physics, or engineering who aspire to apply these skills in financial contexts.
Program Format & Duration
Full-time, in-person program
Duration: 12 months + 10–12 week internship (typically March–March)
Location: Haas School of Business, UC Berkeley
STEM-designated (international students eligible for up to 3 years OPT)
Annual intake: Spring start (March)
Highly competitive admissions process
Core Curriculum Components
The Berkeley MFE program combines theory, quantitative rigor, and real-world application, providing students with the tools to thrive in today’s dynamic financial landscape.
Core Subject Areas
Financial Theory & Instruments
Derivatives and Fixed Income
Risk Management
Portfolio Management
Financial Markets and Institutions
Mathematics & Quantitative Methods
Stochastic Calculus
Optimization in Finance
Numerical Methods for Finance
Time Series Analysis
Data Science & Programming
Machine Learning Applications in Finance
C++, Python, R for Financial Computing
Big Data Analytics
Deep Learning and NLP in Quant Finance
Electives & Workshops
Fintech Innovations
ESG and Sustainable Investing
Algorithmic Trading
Cryptocurrency and Blockchain
Internship Program (Applied Finance Project)
A defining feature of the MFE program is its mandatory internship, typically between October and January (after 6 months of coursework)
Students are placed in top firms across finance and tech, often leading to full-time offers
Supported by Haas' dedicated Career Services and faculty mentorship
Industry Projects & Competitions
Opportunities to work on real-world quant problems with banks, hedge funds, and asset managers
Participation in global competitions such as Rotman International Trading Competition and Citadel Datathons
Professional Speaker Series
Regular sessions with industry leaders from firms like Goldman Sachs, Two Sigma, BlackRock, Google, and Square
Topics cover market trends, AI in finance, and career growth strategies
Graduates of the Berkeley MFE are among the highest-earning and most sought-after professionals in the financial world. They go on to lead in quant finance, tech, and fintech roles globally.
Career Outcomes
Top job titles include:
Quantitative Researcher / Quant Analyst
Risk Manager / Credit Risk Modeler
Algorithmic Trader
Data Scientist (Finance focus)
Structured Products Specialist
Financial Engineer
FinTech Product Manager
Top Employers
Berkeley MFE graduates are placed at elite institutions such as:
Goldman Sachs, Morgan Stanley, JPMorgan
Citadel, Two Sigma, Jane Street
BlackRock, Point72, BNY Mellon
Google, Meta, Stripe (Quant & Data Science roles)
Hedge funds, proprietary trading firms, and AI-focused startups
Salary Outcomes
Average starting salary: $140,000 base (plus signing bonus & performance bonuses)
Over 98% placed within 3 months of graduation
Offers often secured during internship phase
Placement & Salary Overview
Placement rate: 96% received job offers within three months of graduation (Class of 2023); 95% accepted them
Compensation (Class of 2023):
Mean base salary: $147,578
Median base salary: $150,000
High-end base offers: up to $275,000
Mean signing bonus: $29,920
Median signing bonus: $15,000
Signing bonuses ranged up to $110,000
Industries & Functions
Industries:
Investment Banking (44%)
Trading Desks (19%)
Asset Management (13%)
Hedge Funds (10%)
Research & Ratings Agencies (4%)
FinTech / Other (10%)
Functions:
Portfolio Management / Quantitative Research (53%)
Trading (17%)
Quantitative Structuring (16%)
Data Science / AI (7%)
Other Quant Roles (7%)
Top Employers & Geographic Placement
Hiring firms: Balyasny Asset Management, BlackRock, Goldman Sachs, JP Morgan, Morgan Stanley, Millennium, Citadel, Deutsche Bank, Tower Research, GTS, Charles Schwab, among others
Location distribution:
East Coast (62%), including NYC/Boston/Miami
Chicago (11%)
California (8%)
International placements (10%)
Other U.S. locations (8%)
Program Strengths & Career Advantage
One-year, STEM-designated curriculum with 28 units, including core courses, an internship (10–12 weeks), and an applied finance team project
Intensive career support beginning at orientation: mock interviews, resume workshops, employer networking, and strong alumni engagement
Outcomes Summary Table
| Aspect | Details |
|---|---|
| Placement Rate | 96% received offers; 95% accepted by 3 months |
| Base Salary (Mean/Median) | $147,578 / $150,000 |
| Signing Bonus (Mean/Median) | $29,920 / $15,000 |
| Salary Range | Up to $275,000+ |
| Main Industries | Investment Banking, Trading, Asset Mgmt, Hedge Funds, FinTech |
| Top Employers | GS, MS, Balyasny, BlackRock, Citadel, etc. |
| Geographic Spread | 62% East Coast, 11% Chicago, 8% California, 10% Intl |
| Program Highlights | STEM, internship, applied project, dedicated career services |
What This Means for You:
Berkeley MFE graduates benefit from near-universal employment, highly competitive compensation—including multimodal pay packages up to $275K—and strong employer networks in finance and data science. The intensive, practical curriculum and robust career services equip graduates to enter financial engineering and quantitative analytics roles with confidence and clarity.
Bachelor’s Degree: Hold or expect to hold a bachelor’s degree (U.S. 4-year or international equivalent) from an accredited institution—Indian 3-year degrees require a completed master’s to qualify.Academic Transcript & GPA: Upload scanned official transcripts showing a strong quantitative record, typically with ≥3.0 GPA in upper-division coursework.Application Fee: Pay a $275 non-refundable application fee via credit card or check/money order.Prerequisite Coursework: Demonstrate or plan to complete prerequisites in multivariable calculus, linear algebra, differential equations, numerical analysis, advanced statistics/probability, and programming (C++/Python), plus finance and communication skills.Standardized Test Scores: Submit GMAT or GRE scores within 5 years—waived only for PhD holders or U.S. bachelor’s (≥3.70)—and provide TOEFL ≥100 or IELTS ≥7 (no TOEFL MyBest or Duolingo) if eligibleLetters of Recommendation: Submit two recommendation letters from academic or professional evaluators familiar with your quantitative abilities.Essays & Video Essay: Complete written essay prompts plus a ≤2-minute video essay via webcam as part of the application.Interview (If Invited): Participate in an invitation-only interview covering your background and technical preparations.Program Fit: Candidates typically have strong quantitative backgrounds in finance, economics, statistics, mathematics, engineering, or computer science.Application Deadlines: Apply for Spring entry by one of four rounds with deadlines (e.g., R1: Jan 16; R2: Apr 1; R3: Jun 5; R4: Oct 1), with decisions ~9 weeks post-deadline.Three‑Year Bachelor’s Degrees: Applicants must have a U.S.-equivalent bachelor’s degree (4‑year)—three-year degrees from international institutions are not automatically accepted and should be confirmed with the admissions office .WES Evaluation: Official transcripts sent directly from your institution are required, and WES or other credential evaluations are not accepted in place of these, though you may use them for your own verification Portfolio Manager / Quantitative Researcher Structured Products Trader / Trading AnalystQuantitative StrategistResearch Analyst / Ratings AnalystData Scientist in finance and AI-focused rolesQuant Engineer / Developer Data AnalystData ScientistBusiness AnalystStatisticianStatistical ProgrammerSAS ProgrammerMachine Learning EngineerDeep Learning EngineerAI ResearcherData EngineerBI DeveloperData ArchitectAnalytics ConsultantAdvisory ConsultantQuantitative AnalystRisk AnalystOperations AnalystMarketing AnalystResearch ScientistInsights AnalystForecasting AnalystHealthcare Data AnalystFinancial Data AnalystProduct Analyst
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