Lead a Financial Crime product stream, building out a completely new data function and team from scratch, which resulted in line managing a team of 3 Analytics individual contributors to look after the following: - Model development- Dashboards and automated reporting - Insights to drive Group financial crime strategy initiatives- Model Governance

The Advanced Analytics and Innovation team sits within Financial Crime Compliance and acts as a centralised analytics/data science hub. Jamie was a Senior Analyst within the team and helped build a number of models and solutions to aid smart detection and facilitate data analytics maturity within the division.Key skills include: - R- Git- SQL- SAS- Tableau

Big 4 Bank Remediation - Created a POC using R Shiny and Markdown which ingests a Financial Advice Remediation Case assessment file in excel and generates a pdf Report output.Big 4 Bank Remediation - A high profile remediation project identifying a cohort of clients across 3 different financial advice platforms who were eligible to participate in a Corporate Action but were unaware due to the negligence of their financial adviser. In addition to identifying eligibility, a detriment calculation engine was built to determine the amount owed to the client. All the logic was built in Teradata, but also leveraged data from MSSQL and Oracle SQL.

Global General Insurance Company - Developed an operational dashboard in Power BI enabling managers to effectively manage their staff's workload as well as identifying in excess of $600,000 worth of savings through payment remediation. The backend data modelling was in Microsoft SQL using in excess of 90 different tables.KPMG Payroll Analytics - A key developer and contributor to KPMG's Payroll Analytics solution which has been used across several audit and tax clients. This tool consists of several SQL scripts which clean a client's payroll timesheet and payslip data and calculates the amount that they have underpaid/overpaid their staff. A frontend dashboard was also built in Power BI.

- Used SQL extensively to analyse data as well as build stored procedures in their Microsoft SQL Server database to automate numerous manual processes.- Wrote reports in Confluence and JIRA to assist Software Engineers to identify areas of improvement in their port scheduling solutions/algorithms.- Frequently used the programming language R for exploratory data analysis, data cleansing, and visualisation using GGplot2 for the previously mentioned reporting.- Applied a number of Machine Learning (mostly classification) algorithms such as Logistic Regression, Decision Trees, Random Forest and Support Vector Machines to predict positive/negative outcomes in Port Scheduling Solution.