Company Description
Visa is a world leader in payments and technology, with over 259 billion payments transactions flowing safely between consumers, merchants, financial institutions, and government entities in more than 200 countries and territories each year. Our mission is to connect the world through the most innovative, convenient, reliable, and secure payments network, enabling individuals, businesses, and economies to thrive while driven by a common purpose – to uplift everyone, everywhere by being the best way to pay and be paid.
Make an impact with a purpose-driven industry leader. Join us today and experience Life at Visa.
Job Description
The Model Risk Management (MRM) team at Visa has a mandate to validate business-impactful models firm-wide and more generally to identify, measure, and handle model risk across Visa. The Senior Consultant, Model Risk Management will be responsible for conducting and assisting in model validations of models to strengthen model risk management capabilities at Visa.
You will be responsible for:
* Identifying sources of risk in models. With minimal supervision conduct or assist with conducting validations of quantitative and complex financial models including AI/ML. Thoroughly and comprehensively review all model components and developmental evidence. Responsible for providing and documenting effective challenges to conceptual soundness of models and conducting quantitative analytics.
* Maintaining detailed and comprehensive records of validation projects through work papers and other acceptable project artifacts.
* Tracking and reviewing model performance monitoring results. Review evidence for closure of model validation findings.
* Building strong working relationships with key model stakeholders, in particular, model developers and users.
* Supporting activities of Model Risk Management Working Group in determining agenda, meeting minutes, etc.
* Keeping abreast of new modeling techniques by actively attending training sessions/courses.
* Developing code libraries for routine tasks to increase efficiency of all validators.
* Providinginput and train all Model stakeholders in the MRM procedures and use of the GRC tool, Global Risk Oversight tool (GoRO).
This is a hybrid position. Expectation of days in office will be confirmed by your Hiring Manager.
Qualifications
Basic Qualifications:
* 8 or more years of relevant work experience with a Bachelor’s Degree or at least 5 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 2 years of work experience with a PhD
Preferred Qualifications:
* 9 or more years of relevant work experience with a Bachelor’s Degree or 7 or more relevant years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 3 or more years of experience with a PhD
Required experience:
* Advanced knowledge of quantitative models obtained through advanced degree (PhD, MS, or MFE) and/or prior work experience
* Strong quantitative skills and practical experiences in areas such as financial theories, Deep Learning, Machine Learning, Statistical or other mathematical analysis
* Experience reviewing and validating business-impactful models through the lens of conceptual soundness and robustness
* End-to-end processing and modelling of large data sets
* Familiarity with deploying and maintaining of statistical models and algorithms in a production setting
* Excellent critical thinking, writing and verbal communication skills
* High level of competence in Python, Spark, and Unix/Linux scripts
* Demonstrable experience using distributed systems (for example, Hadoop, Hive, Impala)
* Extensive experience with SAS/SQL/Hive for extracting and aggregating data
* Experience with time series modelling problems
* Deep learning experience with TensorFlow
Additional Information
Visa is an EEO Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law.
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