CIB Risk-quantitative Research Resume Sample
Work Experience
- Signal-driven idea generation
- Data-driven trading decision making
- Automatization of derivatives risk management, pricing and inventory optimization
- Write well-formulated documents of model specification and implementation testing
- Rapidly create prototype models and products; benchmark and compare results of various techniques
- Act as QR MRC’s liaison for FRTB and coordinate with market risk management on development of plans, timelines and deliverables
- Explain model behaviour and predictions to traders and controllers, identify major sources of risk in portfolios, carry out scenario analysis, provide guidance / debug analytics
- Mastery of advanced mathematics and numerical analysis arising in financial modeling
- Linear algebra, probability theory, stochastic processes, differential equations, numerical analysis
- Perform large-scale analysis on our proprietary datasets
- Lead forward the CIB-wide inventory optimization initiative, aimed to streamline cross-business inventory exchanges
- Put in place large and scalable architectures, linearize state-space to deal with massive data sets, vectorised coding and distributed computing
- Liaise with multiple stake-holders to formulate a multi-dimensional objective function across metrics: PnL, Liquidity, RWA, ROA, etc
- Model inventory dynamics, transaction costs potentially employing Statistical and Machine Learning techniques
- Contribute to the continuous development of the firm-wide optimization framework
- Work closely with technology on integration of optimization models with front end applications
- Liaise with trading and front office to capture requirements and help drive the development of the library and tools around it, in coordination with the technology group
- Participate in the re-engineering of the equity derivatives risk management systems their and integration into the qRisk and Athena frameworks
- Developing models for the pricing and risk management of equity derivatives, including investigating improvements to existing models
- Implementing these models in our quant library
- Supervising/managing junior team members
- Work with big-data and experience in formulation of optimizations
- Strong experience in linearization of state-space
- Experience with parallel computing, vectorization and memory management is positively regarded
Education
Professional Skills
- Excellent data analysis and statistical modeling experience
- Python, Java, Perl and web programming skills
- Experience in a derivatives modelling environment (front office or model validation)
- Experience in parallel programming, e.g. TBB, OpenMP, CUDA or OpenCL
- Strong C++ coding, with emphasis on numerical methods
- OpRisk and/or economical Capital experience
- Professional C++/Python development experience
How to write CIB Risk-quantitative Research Resume
CIB Risk-quantitative Research role is responsible for modeling, research, trading, integration, finance, programming, web, perl, analysis, inventory.
To write great resume for cib risk-quantitative research job, your resume must include:
- Your contact information
- Work experience
- Education
- Skill listing
Contact Information For CIB Risk-quantitative Research Resume
The section contact information is important in your cib risk-quantitative research resume. The recruiter has to be able to contact you ASAP if they like to offer you the job. This is why you need to provide your:
- First and last name
- Telephone number
Work Experience in Your CIB Risk-quantitative Research Resume
The section work experience is an essential part of your cib risk-quantitative research resume. It’s the one thing the recruiter really cares about and pays the most attention to.
This section, however, is not just a list of your previous cib risk-quantitative research responsibilities. It's meant to present you as a wholesome candidate by showcasing your relevant accomplishments and should be tailored specifically to the particular cib risk-quantitative research position you're applying to.
The work experience section should be the detailed summary of your latest 3 or 4 positions.
Representative CIB Risk-quantitative Research resume experience can include:
- Previous practical experience in solving machine learning problems using open-source packages (sklearn…). Experience in TensorFlow or other RL packages is advantageous
- C++ coding, with emphasis on numerical methods
Education on a CIB Risk-quantitative Research Resume
Make sure to make education a priority on your cib risk-quantitative research resume. If you’ve been working for a few years and have a few solid positions to show, put your education after your cib risk-quantitative research experience. For example, if you have a Ph.D in Neuroscience and a Master's in the same sphere, just list your Ph.D. Besides the doctorate, Master’s degrees go next, followed by Bachelor’s and finally, Associate’s degree.
Additional details to include:
- School you graduated from
- Major/ minor
- Year of graduation
- Location of school
These are the four additional pieces of information you should mention when listing your education on your resume.
Professional Skills in CIB Risk-quantitative Research Resume
When listing skills on your cib risk-quantitative research resume, remember always to be honest about your level of ability. Include the Skills section after experience.
Present the most important skills in your resume, there's a list of typical cib risk-quantitative research skills:
- Solid knowledge with CPLEX, GUROBI, MOSEK or other main stream optimization packages is desirable
- Broad, deep understanding of equity derivatives pricing theory and standard models
- Basic understanding of numerical methods, probability and foundations of quantitative finance to ensure that detailed knowledge can be picked up if required
- Excellence in probability theory, stochastic processes, partial differential equations, and
- Excellence in probability theory, stochastic processes, and numerical analysis
- Confident in technology in particular around data management. Knowledge in KDB and Big Data solutions such as Hadoop/Spark, Hive etc advantageous