CIB Risk-quantitative Research Resume Sample

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Newell Orn
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Work Experience

CIB Risk-quantitative Research Associate
09/2017 - PRESENT
Chicago, IL
  • 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
CIB CIB Risk Quantitative Research
09/2010 - 03/2017
Dallas, TX
  • 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
Cib-risk-quantitative Research
01/2004 - 04/2010
San Francisco, CA
  • 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


Friends University
1999 - 2003
Master's Degree in Math

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
  • Email
  • 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

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