Job
Description
We are looking to hire colleagues with advance data science and analytics skill to support our financial crimes team You will play a crucial role in helping clients tackle the multifaceted challenges of financial crime By utilizing advanced analytics and deep technical knowledge, our team aids top clients in reducing risks associated with financial crime, terrorist financing, and sanctions violations We also work to enhance their screening and transaction monitoring systems Our team of specialized analysts ensures that leading financial institutions adhere to industry best practices for robust programs and controls Through a variety of project experiences, you will develop your professional skills, assisting clients in understanding and addressing complex issues, and implementing top-tier solutions to resolve identified problems Minimum work experience: 3+ years of advance analytics Preferred experience: 1+ years in AML model validation Responsibilities Support functional SME teams to build data driven Financial Crimes solution Conduct statistical testing of the screening matching algorithms, risk rating models and thresholds configured for detection rules Validate data models of AML systems built on systems such as SAS Viya, Actimize, Lexis Nexis, Napier, etc Develop, validate, and maintain AML models to detect suspicious activities and transactions. Conduct Above the Line and Below the Line testing Conduct thorough model validation processes, including performance monitoring, tuning, and calibration. Ensure compliance with regulatory requirements and internal policies related to AML model risk management. Collaborate with cross-functional teams to gather and analyze data for model development and validation. P erform data analysis and statistical modeling to identify trends and patterns in financial transactions. Prepare detailed documentation and reports on model validation findings and recommendations. Assist in feature engineering for improvising Gen AI prompts applicable for automation of AML / Screening related investigations Use advanced Machine Learning deployment (eg XGBoost) and GenAI approaches Criteria: bachelors degree from accredited university 3+ years of complete hands-on experience in Python with an experience in Java, Fast, Django, Tornado or Flask frameworks Working experience in Relational and NoSQL databases like Oracle, MS SQL MongoDB or ElasticSearch Proficiency BI tools such as Power BI, Tableau, etc Proven experience in data model development and testing Education background in Data Science and Statistics Strong proficiency in programming languages such as Python, R, and SQL. Expertise in machine learning algorithms, statistical analysis, and data visualization tools. Familiarity with regulatory guidelines and standards for AML Experience in AML related model validation and testing Expertise in techniques and algorithms to include sampling, optimization, logistic regression, cluster analysis, Neural Networks, Decision Trees, supervised and unsupervised machine learning Preferred experiences: Validation of AML compliance models such as statistical testing of customer / transaction risk models, screening algorithm testing, etc Experience with developing proposals (especially new solutions) Experience working AML technology platforms eg Norkom, SAS, Lexis Nexis, etc Hands on experience with data analytics tools using Informatica, Kafka, etc
