Senior Data Scientist: Model Validation – IT-Online

[ad_1]

A leading financial services organisation has an opportunity for a Senior Data Scientist: Model Validation to join their organization. The role is responsible for the technical review and validation of models used within the organisation, especially machine learning models, ensuring appropriate model governance and model risk management processes.

Responsibilities:

  • Manage model risk relating to the end to end model life cycle, with a special focus on Machine Learning models used across the organization
  • Responsible for managing model risk in respect of the entire model life cycle (design, development, validation, implementation, monitoring, model recalibrations and any other changes) of all models; (with a primary focus on machine learning models developed by the banks Data Science team)
  • Provide expert advice and recommendations to the Head with regards to machine learning modeling to support the achievement of Model Risk managements goals
  • Provide modelling subject matter knowledge, research and expertise to recommend improvements to Model Risk Management generally and machine learning models used in Data Science in particular
  • Take ownership (e.g. network and research) of personal development to remain abreast of advancements and developments in model risk management: technology, best practice and legal/statutory requirements in order to assess whether and ensure that the modelling approaches used follow industry best practice
  • Design and recommend processes which optimise the performance and effectiveness of the Model Risk Management function
  • Stay abreast of regulatory and industry requirements in banking and risk to manage reviews and changes in processes for the abovementioned models and to check that models comply with relevant banking regulatory and legislated requirements
  • Take ownership of the review process for models (including model validations), particularly machine learning models
  • Responsible for prioritizing requirements and executing written technical independent model reviews to manage risk and optimize design and performance
  • Engage with modelling teams regarding existing models as well as future model requirements and plan and provide guidance around models appropriate design, implementation and usage
  • Validate model output by replicating a model or components of a model
  • Perform code reviews
  • Constructively challenge and improve existing models for example by developing challenger models or model components
  • Provide technical challenge at the Model Technical Committee
  • Provide guidance on best practice modelling and monitoring methodologies
  • Apply subject matter expertise (specifically for machine learning models), engage with stakeholders and attend forums and committees to influence model policy and governance processes to ensure model risk is managed effectively
  • Responsible for putting model governance and model risk management processes and standards in place
  • Drive development and use of monitoring frameworks in adherence with Capitec policies and procedures
  • Collaborate and work with modelling teams to create understanding and adoption of model policy requirements and standards
  • Perform reviews and checks to assess adherence to model policy and standards and regulatory and other external model requirements
  • Agree a reporting framework and schedule for model risk with the Head and report on model performance, coverage, limitations, errors, model maturity, non-adherence to policy and other model risks
  • Make presentations on the status of model risk to model owners and senior management as required
  • Interact with modelling teams, business stakeholders, IT and Risk functions, and reporting groups such as committees and forums

About The Employer:

Requirements:

  • Bachelor’s Degree in Mathematics/Statistics/Actuarial Science/Informatics/Quantitative Risk Management
  • 5 Years Machine Learning Model Development Expereince

Knowledge and understanding of:

  • Machine learning model development
  • Underlying theory and application of machine learning models, must be able to understand underlying principles and theory and be able to teach others
  • Best practices for data science
  • Ethical AI design principles
  • Machine learning model architecture (technical design and implementation processes)
  • Source control systems e.g. Git, Bitbucket, or Sourcetree
  • Analytics
  • Statistical and predictive methodologies
  • Table and database structures
  • Statistical software (e.g. SAS, R, Python)
  • Database querying (e.g. SQL)
  • Advanced Microsoft Excel (including VBA)
  • Technical understanding and knowledge of different operating systems / databases / programming languages
  • Model risk management and model governance.
  • Understanding of technical issues and the impact these may have on other models, strategies, decisions and areas
  • Reporting at strategic level for senior management/executives

Kindly note that all positions will be filled in accordance with our client’s Employment Equity plan. We also encourage people with disabilities to [URL Removed] you not receive feedback on your application with us within a period of 2 weeks of submission, you may consider your application as being unsuccessful. Please keep an eye on our website and other career sites for future opportunities that may arise.POPIA Disclaimer: Please take note that by responding to this advertisement and providing your personal information in application thereof, you confirm your express and informed consent for Persaf Holdings (Pty) Ltd and all its subsidiaries and all affiliated companies to process your personal information; to retain your personal information on our database for future matching; to contact you when suitable opportunities arise; and that the information you have provided to us is true, correct and up to date.

Learn more/Apply for this position


[ad_2]

Read More:Senior Data Scientist: Model Validation – IT-Online