
Senior Consultant, Model Risk Management
- London
- Permanent
- Full-time
- 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.
- Providing input and train all Model stakeholders in the MRM procedures and use of the GRC tool, Global Risk Oversight tool (GoRO).
- 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
- 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
- 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