Financial & Data Modeler job at DFCU Bank

Description

Reporting to the Head of Data & Insights, the role is responsible for developing financial models to support business strategy and improve decision-making accuracy, as well as sustaining the models in adherence to the Model governance framework.

Duty Station: Head Office

KEY ACCOUNTABILITIES:

• Design/develop/validate/deliver/review/maintain and enhance financial models in the following risk areas, Credit risk (e.g., AIRB, IFRS 9, Early warning, and credit scoring models), Market risk (e.g., VAR (Value at Risk), EAR (Earnings at Risk) models), Operational risk, Price risk (e.g., Risk based pricing models) and Liquidity risk based on industry best practices.
• Support and work collaboratively with other quantitative and analytical areas such as forecasting and stress testing, customer behaviour modelling, and new innovations such as Machine Learning and Artificial Intelligence.
• Train end users to ensure that they are well equipped to run and make strategic decisions from model outputs.
• Prepare and Communicate model development documents (e.g., Model development plans, Model development technical documents and User guides) to key stakeholders.
• Ensure adherence to the model development policy, data governance framework, policies, and procedures.
• Ensure financial models adhere to the defined and agreed service level agreements (SLAs).
• Keep up to date with the latest technical developments within financial modelling and engineering and making sure that changes to the industry’s best practice are adopted project team.

KNOWLEDGE, SKILLS, AND EXPERIENCE REQUIRED:

• Bachelor of Science or master’s degree in Mathematical Finance, Financial Engineering, Mathematics, Statistics and Quantitative Economics/Econometric.
• Professional qualification in such as (FRM, CFA, ACCA) is an added advantage.
• Other qualifications, certifications, or professional memberships that relate to financial engineering, financial modelling, data Analytics, Machine Learning and Data Governance.
• One to two years of relevant experience in Credit Risk, Market Risk model development or model validation/vetting team at a major Financial institution.
• Knowledge of Financial products and their modelling and calibration in both risk–neutral and real world across a wide range of products.
• Creativity in problem solving.
• Excellent Mathematical and Statistical skills.
• Solid knowledge of common practices in credit risk, marker risk and operational risk, including (PD, LGD, EAD, VAR, LDA, Economic Capital) methodologies.
• Solid programming skills (e.g., Python/R, SQL, Visual Basic). Knowledge of other programming languages is an added advantage.
• Experience with numerically stress testing techniques is an asset.

• Design/develop/validate/deliver/review/maintain and enhance financial models in the following risk areas, Credit risk (e.g., AIRB, IFRS 9, Early warning, and credit scoring models), Market risk (e.g., VAR (Value at Risk), EAR (Earnings at Risk) models), Operational risk, Price risk (e.g., Risk based pricing models) and Liquidity risk based on industry best practices.
• Support and work collaboratively with other quantitative and analytical areas such as forecasting and stress testing, customer behaviour modelling, and new innovations such as Machine Learning and Artificial Intelligence.
• Train end users to ensure that they are well equipped to run and make strategic decisions from model outputs.
• Prepare and Communicate model development documents (e.g., Model development plans, Model development technical documents and User guides) to key stakeholders.
• Ensure adherence to the model development policy, data governance framework, policies, and procedures.
• Ensure financial models adhere to the defined and agreed service level agreements (SLAs).
• Keep up to date with the latest technical developments within financial modelling and engineering and making sure that changes to the industry’s best practice are adopted project team.

• Creativity in problem solving.
• Excellent Mathematical and Statistical skills.
• Solid knowledge of common practices in credit risk, marker risk and operational risk, including (PD, LGD, EAD, VAR, LDA, Economic Capital) methodologies.
• Solid programming skills (e.g., Python/R, SQL, Visual Basic). Knowledge of other programming languages is an added advantage.
• Experience with numerically stress testing techniques is an asset.

• Bachelor of Science or master’s degree in Mathematical Finance, Financial Engineering, Mathematics, Statistics and Quantitative Economics/Econometric.
• Professional qualification in such as (FRM, CFA, ACCA) is an added advantage.
• Other qualifications, certifications, or professional memberships that relate to financial engineering, financial modelling, data Analytics, Machine Learning and Data Governance.