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Data Analysts within this team are responsible for understanding the business area, data and existing systems and providing solutions that require the application of Data Analytics. Key areas range from supporting existing Transaction Monitoring system optimization (sampling, hypothesis testing, multivariate regression analysis), business segmentation/clustering (K-Means etc), generation of complex insights and actionable MI (data manipulation & BI tools) - all in the pursuit of enhancing the effectiveness and efficiency of our AML program and wider Financial Crime controls.
Our key stakeholders and partners include;
the AML Transaction Monitoring team, responsible for the day-to-day management, optimization, tuning and governance of vendor Transaction Monitoring systems such as Oracle Mantas AML and NICE Actimize SAM;
the AML Investigations team, responsible for case investigations;
the Financial Intelligence Unit, responsible for conducting complex thematic investigations in relation to new and existing AML threats.
Business Unit Advisory, managing all Financial Crime Risks with the 1st Line of Defence.
To achieve our goals, the team works with our technology partners, Financial Crime/Compliance Transformation, which provide services in the development of data and technology architecture - such as the implementation and technical management of Transaction Monitoring systems and the development of data architectures and analytics platforms.
Support the growth and scope of the Financial Crime Analytics & Data Science team through the generation of ideas. This will involve engaging with key stakeholders to identify their key problems and needs, and keeping up-to-date with external industry development through own research and attending key peer-group meetings and conferences. Presentation of key findings to stakeholders at the right levels are also of great importance.
Provide support to aid in the development of junior team members both from a technical perspective and provided business requirements -driven direction.
Provide hands-on analytics support to the AML Transaction Monitoring team in areas such as threshold tuning/optimization, customer/account segmentation and data-driven decision making and insights. This will involve techniques such as hypothesis testing, regression analysis, optimization methods and clustering analysis.
Engage in a range of innovative PoC/Prototype development activities including the data-driven automation of various currently manual processes, development of interactive dashboards, the development of alert & case scoring and prioritization models and the generation of enhanced AML detection capabilities through the application of machine learning techniques.
Provide analytics support to the AML Investigations team in areas such as the development of case-prioritization scoring processes, enhanced alert-case merging and ad-hoc insight requests.
Support in activity relating to the Banks Model Risk Management policy where required.
Engage with our internal Technology team to provide requirements on the development of strategic data infrastructure ensuring that our infrastructure capability aligns to the needs of the Financial Crime Analytics team as well as to the needs of our wider stakeholders.
Assist and manage the running of regular analytical processes.
You will have the academic background, work experience and interests/understanding to consider yourself an experienced Data Analyst with the ability to;
Quickly learn/adapt to new business area(s), data systems and technologies;
Use your experience to proactively identify problems that need a data-driven solution;
Apply your technical skill-set in designing and building such solutions;
Effectively communicate (written and verbal) these solutions to senior management.
Existing experience in the application of analytics within the area of Financial Crime will be advantageous.
Essential Skills/Basic Qualifications:
A Bachelors degree in a quantitative discipline with a significant Statistics component (Statistics, Mathematics, Operational Research, Business Analytics, Computer Science, Computational/Mathematical Finance, Physics, Economics/Econometrics).
Experience within a large corporate/Financial Services institution beneficial & some experience in the application of statistical analysis around Financial Crime/AML Transaction Monitoring will be seen as favourable.
Experience in the use of statistical analysis and data manipulation tools (SAS, SQL, R, Python, Tableau) - SAS and Tableau experience preferred.
Some experience in applying a wide range of statistical and machine learning techniques (e.g. hypothesis testing, regression, clustering, decision trees, machine learning models).
Desirable skills/Preferred Qualifications:
Knowledge of Financial Crime legislation (specifically Anti-Money Laundering / Terrorist Financing), exposure to common AML Transaction Monitoring systems, and an understanding of the application of analytics in the Financial Crime space will be seen as a plus.
Experience in managing the scheduled running of regular processes, in SAS and SQL, would be beneficial.
Experience with visualization tools (e.g., Tableau, Sportfire, Qlikview) beneficial.
Exposure/Experience with distributed-data architecture (Hadoop/MapReduce, Spark) and cloud architecture (e.g. AWS) a plus