Data Science Role - Banking

Job Description

Drive innovation through advanced analytics (machine learning, predictive analytics) on an ongoing basis. Passionate about exploring data opportunity using analytics techniques in enhancing efficiency and effectiveness of AML in the areas of transaction monitoring and emerging risks.

Key Responsibilities:

- Apply rigorous analytical methods to organize, analyze, and interpret data and reports; ensure appropriate methodology is selected to draw conclusions and make recommendations answering specific business needs

- Identify problematic areas and conduct research to determine the best course of action to correct scenario setting or data

- Drive initiatives while collaborating with regional and global team and liaising with stakeholders for effective support

- Emerging and Monitoring Risk analytics

- Provide AML scenario expertise in projects such as scenario addition, new monitoring and intelligence

- Think outside the box and look for new way to constantly improve the effectiveness and efficiency of the AML Detection, Monitoring and Investigations systems and processes

- Innovate and generate new ideas, concepts and models to institute emerging risk analytics strategy/process and execution

- Be a part of the strategic initiative in rolling out Central Intelligence and Future State of AML

- Document solutions and present results in a simple comprehensive way to non-technical audience, as well as write more formal documentation using statistical vocabulary

- Maintain an industry leading knowledge of the tools, systems and processes available for best in class AML Detection, Monitoring and Investigation

Qualifications

Qualification and Skills

- Minimum of 9+ years of overall experience and relevant experience in AML/Fraud/Compliance Risk areas

- Knowledge on AML and Mantas

- Strong interpersonal skills with ability to work with cross-functional and cross-cultural team. This role will require to influence stakeholders

- Technical expertise regarding data analytics in the context of AML typology, risk, monitoring improvement and recommendation

- Strong analytical skills in data driven analysis with prior experience in machine learning and data algorithm technique (network science, recommendation engine, multivariate regression, text mining)

- Experience in data mining tools/techniques and procedures and knowing when their use is appropriate

- Required experience with relational databases such as Oracle, SQL Server and Big Data

- Ability to present complex information in an understandable and compelling manner

- Knowledge of the local/regional laws and regulations applicable to anti-money laundering is a plus

- Cross-industry data analytics application and knowledge is a plus

- AML Investigation operational knowledge is also an advantage