A top-tier global bank seeks a Data Scientist for their Financial Crimes Analytics division.
The Financial Crimes team helps ensure that the bank complies with Anti-Bribery & Corruption (ABC), Anti-Money Laundering (AML) and Sanctions legislation. Within Financial Crimes, the Analytics team provides support in areas requiring analytics of a quantitative/statistical nature by deploying expertise in data analytics, statistical inference/modeling and risk management. Data Scientists within this team are responsible for understanding the business area, data and existing systems and providing solutions ranging from Transaction Monitoring system optimization, the development of alert prioritization/scoring models and algorithms, generation of complex insights and actionable MI – all in the pursuit of enhancing the effectiveness and efficiency of the AML program.
The 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.
To achieve their goals, the team works with 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.
This position provides a great growth opportunity for a junior Data Scientist. Although some hands-on analytics experience in a corporate environment will be seen as advantageous, as a junior Data Scientist you will be provided with the opportunity to develop the wide array of skills and experiences needed through working alongside more experienced Data Scientists and relevant training opportunities.
Key Accountabilities and Skills required:
- Provide 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 R&D activities including the data-driven automation of various currently-manual processes, the development of alert/case scoring & prioritization models and the generation of enhanced AML detection capabilities through the application of traditional statistical models (regressions etc) and more complex machine learning techniques.
- Support the growth and scope of the Financial Crime Analytics 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.
- 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.
You will have the academic background, interests/understanding and some experience to be on the path to becoming a Data Scientist with the motivation to;
- Learn/adapt to new business area(s), data systems and technologies;
- Build the experience to proactively identify problems that need a data-driven solution;
- Develop a technical skill-set that allows you to design and build such solutions;
- Effectively communicate (written and verbal) these solutions to senior management.
You will be provided with the opportunity to develop the wide array of skills and experiences needed through working alongside experienced Data Scientists and training opportunities.
Basic Qualifications, Skills & Experience
- A Bachelors in a quantitative discipline with a significant statistical/data analytics component (Statistics, Mathematics, Operational Research, Computer Science, Computational/Mathematical Finance). A Masters degree in Data Science or similar preferred.
- 1 year relevant experience. Experience within a large corporate/Financial Services institution beneficial.
- 1 year hands-on experience in the use of statistical analysis and data manipulation tools (SAS, R, Python) – advanced SQL skills are crucial and some experience in Python is preferred.
Preferred Qualifications, Skills & Experience
- Understanding of/exposure to a range of statistical and machine learning techniques (e.g. hypothesis testing, regression, clustering, decision trees, machine learning models).
- Exposure to common Python libraries for data manipulation, statistical analysis and machine learning (Pandas, Scikit, TensorFlow, h2o.io etc) desirable.
- Experience with visualization tools (e.g., Spotfire, Tableau) beneficial.
- Exposure/experience with distributed-data architecture (Hadoop/MapReduce, Spark) and cloud architecture such as AWS a plus.
Please email resumes to Jack@ComplianceSearch.com