Job Description
Goldman Sachs Strats business unit is a world leader in developing quantitative models and technologies to solve complex business problems. Working within the firm's trading, sales, banking and investment management divisions, strats use their mathematical and scientific training to create financial products, advise clients on transactions, measure risk, and identify market opportunities.
Your Impact
FICC Credit Exotics Strats sit within the firm's Global Markets Division and works directly with Credit Exotics trading desks globally to develop and uplift pricing models & improve risk management framework increasing our market share and efficiency.
As a member of the team, you will work closely with global Exotics traders to build and improve pricing & risk models for complex credit linked financial products. You will also implement and develop risk management infrastructure to assist in monitoring and managing risk by desk traders as well as senior management.
You will also partner with global trading/controllers/credit risk teams for uplifting pricing engines to optimize capital footprint in light of recent/upcoming industry regulations. In addition, you will assist senior management, business in enhancing end-to-end risk monitoring & risk management framework by developing new analytics & uplifting existing pricing models for the desk.
Responsibilities
- Building and improving quantitative models, pricing, risk management and workflow infrastructure for the FICC Credit Exotics trading business
- Systematic and quantitative analysis of risk, pricing, PNL metrics for credit exotics products ranging from bonds to vanilla and exotic derivatives(CLNs, Repacks etc.)
- Helping trading desk with daily trading activities, risk management, analyze trade ideas and hedging strategies
- Analysis and model development for cross-business initiatives such as capital optimization and regulatory changes
Candidate will actively collaborate with colleagues not only in Bengaluru but also with the desk strats and trading team globally.
Who We Look For
An ideal candidate would have strong quantitative and technical problem solving skills, drive to investigate and learn new ideas, and good judgement to deliver quick yet robust solutions.
Basic Qualifications
- Strong academic background in a relevant STEM field (Computer Science, Engineering, Physics or Mathematics)
- Strong quantitative and programming skills (Java, C++, Python)
- Strong interpersonal/communication skills
- Ability to focus both on details and on the big picture
- Ability to work in a dynamic and fast- paced environment and deliver accurate results quickly
- Ability to solve problems and to explain underlying ideas
Preferred Qualifications
- Knowledge and understanding of financial markets, financial modeling, a quantitative understanding of probability and stochastic calculus
About Goldman Sachs
At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world.
We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers.
We're committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html
The Goldman Sachs Group, Inc., 2023. All rights reserved.
Goldman Sachs is an equal employment/affirmative action employer Female/Minority/Disability/Veteran/Sexual Orientation/Gender Identity
