- Conduct data analysis for tuning and optimization of various models and tools of Sanction Scanner.
- Conduct the model development for transaction monitoring.
- Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation.
- Generate new ideas, concepts and models to improve methods of obtaining and evaluating quantitative and qualitative data. Identify relationships and trends in data, as well as any factors that could affect the results of research. Question and validate assumptions.
- Responsible for day to day activities within Sanctionscanner Operations and Analytics, specifically supporting data quality, sanctions screening,
- Conduct data quality analysis; analyze data for trends and anomalies and present findings.
- Manage daily sustain activities and project initiatives supporting AML technology.
- Support all AML technology including user acceptance testing.
- Understanding of AML regulatory requirements, MASAK, FATF, EU Directives etc.
- Miscellaneous duties as assigned.
- Strong academic qualifications with a degree in a Computer Science, Engineering, Statistics, Mathematics, etc. subject or equivalent work experience for min 2 years.
- Strong critical thinking, problem-solving skills, understanding of algorithms and appreciation of working with data.
- Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations.
- Experience diving into data to discover hidden patterns.
- Coding experience in Python and R.
- Experience with relational databases, e.g. SQL Server, Big data technologies such as Hadoop, Spark.
- Experience with Predictive Analytics, Machine Learning and advanced analytics (such as clustering analysis, decision tree algorithms, neural network, social network analysis, support vector machine).
- Domain knowledge of finance, fraud, anti-money laundering, bribery and corruption or sector specific knowledge or experience (preferable).
- Enthusiasm to learn and develop emerging technologies and techniques.
Position InfoCompany Sector Information Technologies
Employee Type Full Time