• Analysis of rich user and transaction data to surface patterns, trends, and bugs with regional processes and contribute to fraud prevention mechanisms.• Optimize fraud rules & algorithms to maintain strong fraud detection by rapidly identifying emerging fraud trends through data-driven analysis and develop tactical/strategic fraud rules to address them.• Perform data/statistical analysis to keep Fraud systems and processes at the cutting edge of fraud detection by identifying areas of potential fraud risk and/or potential opportunity to improve fraud policies, strategies and controls.• Analyze the effectiveness of existing fraud models and oversee the design, development, and management of new real-time fraud rules and models.• Develop and communicate insights and recommended actions to stakeholders to manage risk by contributing towards machine learning models, rules and other detection systems.• Build & maintain dashboards for all stakeholders to provide visibility of key metrics, fraud patterns and detection efficiency.• Build & maintain data pipelines required by upstream and downstream teams to manage risk related processes.• Be the source of truth for risk metrics in the organization and own their logic as well as monitoring.• Envision and create alert mechanisms and automated outputs which keep stakeholders aware of movement and deviations.
Job Requirements
Proficient in English for written and verbal communication.
Bachelor's degree in Computer Science, Statistics, Engineering, or other related quantitative fields. Masters strongly preferred.
Minimum 3 years of hands-on experience in database query using SQL.
Experience in data science and machine learning using Python (preferred) or R.
Hands-on experience in all stages of the machine learning development lifecycle, including data wrangling, feature engineering, training, validation, and model tuning.#J-18808-Ljbffr