Work collaboratively with the wider analytics team and platform to provide collective insight and value for stakeholders. Answer key analytics questions by developing, implementing and maintaining models in areas such as following.

• Predictive modelling for product and conversation recommendation.
• Customer life time value modelling to predict the customers' future value to the bank.
• Customer Segmentation for grouping customers together based on comparable characteristics relevant to business need around life stage, spend and product appetite.
• Churn Modelling to understand the time before a customer drops a product or leaves the bank
• Support members of the wider Analytics community using Data Science techniques.
• Work to technical standards, tooling, policies and guardrails for my Chapter.

Works at ASB Model Assurance, Financial Risk and Compliance (Line 1 Risk) of Financial Services division.

• Independent end to end validations on critical pan bank models and quantitative tools following ASB Model and Tools Validation Standard and consistent with Model Risk Policy.
• Liaise with model owners and work collaboratively with various teams to understand the model and follow end-to-end information through the an input, model to an output.
• Perform model diagnostics and quantitative analysis to understand the soundness of the model. Appropriately challenge the modelling results, the thought process around the choice of methodology, and the interpretation of relevant regulatory and internal requirements to enable stakeholders to understand the model’s strengths, weaknesses, and limitations.
• Support the model owners and divisional Line 1 Risk team to understand, raise and formalise action plans relating to issues/incidents identified during the validation process. Escalate to relevant stakeholders and senior management when required.
• Document the investigation evidence and findings thoroughly and write up validation report.

Maintain, develop, and operate Treasury’s microsimulation model of the tax and welfare system.

• Support advice on tax and welfare system by modelling policy changes and assessing income, costs, poverty, inequality and distributional impacts at the individual, family, and household levels.
• Build empirical foundations for Treasury’s policy advice in partnership with policy teams both within NZ Treasury and other government agencies such as the Ministry of Social Development and Inland Revenue.
• Provide insight allowing the government to make inform decisions that will improve the living standards for all New Zealanders.
• Investigating and linking between various types of survey and administrative datasets within Statistics NZ’s IDI databases for the microsimulation model.
• Developed 2 Shiny Dashboard applications, in showing income compositions and distributions, 5 internal R packages, containing R functions for repetitive processes, and 1 Rmarkdown template for automatic reports.

Providing statistical services to clients from both within and external to the University of Auckland.

• Worked for more than 60 clients ranging from medical professionals to commercial companies, providing both expert advice and analytical services.
• Supported 5 different teams/departments across Faculties of Science, Arts, Medicine, and Business at the University of Auckland.
• Taught 6 introductory R workshops – with lectures and hands-on exercise - over a period of 3 years in Auckland and Wellington to academic, government and corporate participants.
• Developed 4 web-deployed Shiny applications for the Centre of Methods and Policy Application in the Social Sciences (COMPASS), University of Auckland.