Quant C++

Job Description :

Sample 1:

A Degree in a Quantitative Field: An undergraduate degree is required. An advanced degree is a plus, but mathematical maturity matters far more than formal qualifications.
A minimum of 5 years of industry financial modeling experience, including at least 2 years focused on interest rate modeling. Experience in the muni (municipal bonds) space.
Ability to write robust and efficient code implementing the models. We build analytics using C++; however, expertise in using a different object-oriented language and writing performant code may be sufficient.
A strong analytical mindset is essential. Whether you hold a PhD in Physics or enjoy reading math books for fun, a keen interest in solving complex analytical problems is crucial.
Excellent communication skills are required. You will be expected to explain complex concepts to non-technical people in simple, intuitive terms.
Sample 2:

Quantitative Modeling and Research (QMR) is an innovative team within Single Security Modeling area.
We specialize in crafting sophisticated risk and valuation models that span a diverse range of products, including interest rates, FX, inflation, equity, and credit.
Our mission goes beyond traditional quantitative models; we are at the forefront of exploring novel modeling techniques, such as neural networks, to tackle complex problems in quantitative finance.
Analyze exiting estate of models in rates, FX, inflation, and credit spaces to identify weaknesses that require model revisions.
Work with the model owners to set up concrete model methodology and implementation plans.
Work on designing and setting up modeling, testing, and surveillance processes to ensure compliance with the model risk governance policies.
Review existing model documentation and work with junior team members to bring the documentation to compliance with the model risk governance policies.
Keep abreast of recent trends in quantitative finance, capital markets and government regulation.
What We Look For

Advanced degree in quantitative disciplines like mathematics, physics, engineering, or similar.
Strong practical knowledge of products, modeling methodologies, and analytics in the rates and derivatives space.
Experience in building valuation and testing platforms and tools in this area.
Knowledge of model risk governance practices.
Experience in working with modeling software infrastructure
Ability to work horizontally across various functions, including portfolio managers and traders, quantitative modelers, model risk governance, RQA.
10+ year of industry experience
Sample 3:

This team specifically is building out a new engine for the joint simulation of the global macro economy, drivers of financial markets, and individual assets.
The team is building and connecting innovative models and methodologies across these spaces in a Bayesian framework.
The engine is used in scenario analysis and portfolio construction / strategic asset allocation.
Doing theoretical research to come up with new or find existing models and methodologies in the pricing and risk space, across multiple asset classes including private assets.
Doing empirical research to calibrate new models to financial data.
Back-testing, documenting, and guiding new models and methodologies through validation.
Implementing and maintaining production codebase. Owning the model and managing the use cases in front of stakeholders.
Additional team responsibilities may include working with portfolio management teams on bespoke projects supporting their investment processes or working with
Financial advisory teams on modeling projects for bespoke products.
Qualifications:

Master with 1-3 YOE in Financial Engineering, Mathematics Finance or PhD in Mathematics, Statistics/Econometrics, Science, or other relevant quantitative disciplines.
Hands-on experience with frequentist and/or Bayesian statistics in time-series analysis. Knowledge of machine learning.
Knowledge of financial mathematics (derivatives pricing).
Able to communicate quantitative information and collaborate effectively in a team environment.
Solid programming skills in Python and a drive and ability to quickly pick up new technologies. Exposure to Git, Unix, or any high-performance computing language is a plus but not required. Exposure to PyTorch/TensorFlow/Jax is a plus but not required
Exposure to private equity, private credit, Kalman filter/smoother is a plus but not required
Sample 4:

Lead model governance for Aladdin private markets models including (but not limited to) private equity, private credit, real estate, infrastructure, hedge funds, etc.
Building and maintaining model governance controls, including (but not limited to) model performance monitoring,
Model documentation, model remediations and supporting internal & external client model validations
Communicate (verbally and in writing) with internal stakeholders and external clients on model performance regularly,
Investigate exceptional model performance, diagnose issues and conduct corrective remediations
Doing empirical research to calibrate new models to financial data. Back-testing, documenting, and guiding new models and methodologies through validation
Partner with engineering teams to migrate private markets models onto state-of-art production systems
5-8 years of experience in quantitative field / statistical modeling. Experience with portfolio risk analytics, private markets investments, and /or model governance is strongly preferred
Advanced degree in a quantitative discipline – master’s degree in finance / economics / statistics / financial engineering / math finance, etc.
Knowledge of investments, portfolio management,  econometrics, and empirical asset pricing
A strong background in quantitative research
Hands-on experience with statistical modeling through software (e.g., Python, R, MATLAB) and strong background in programming.  Proficiency with Python is required
Experience with data handling (ETL, data joining with SQL, cleaning, processing, summarizing, descriptive analysis), and building and back-testing statistical and econometric models
Experience with any version control system (e.g., git) is strongly preferred
Prior work experience in financial modeling (e.g., risk models, analytics, private markets)
Model deployment to production environment is a plus