About the role
AI summarisedThis role is for a Big Data Analyst in the Fraud Detection Analytics team at a bank, focusing on protecting customer assets through data analytics and machine learning models. Responsibilities include managing data sources, developing analysis plans, optimizing fraud detection rules, and creating data visualizations. The role requires collaboration with cross-functional teams and staying updated on industry best practices.
BusinessFull-timeGeneral
Key Responsibilities
- Manage and maintain data sources, ensuring the integrity and accuracy of the data that fuels our fraud detection models.
- Develop and execute data analysis plans to extract insights from large and complex datasets, uncovering hidden patterns and anomalies that may indicate fraudulent activity.
- Housekeeping fraud detection rules, including deactivating or tweaking existing ones to reduce false positives (FP) and the overall number of rules, can optimize the rule engine.
- Create and deliver compelling data visualizations and reports that communicate findings to stakeholders, empowering them to make informed decisions and take swift action to prevent fraud.
- Collaborate on multiple high-impact projects and work closely with domain experts from cross-functional teams to drive business value through data-driven solutions.
- Collaborate effectively with cross-functional teams to identify and solve complex business problems using data, fostering a culture of innovation and teamwork.
- Maintain proper documentation of business requirements and data management definitions, ensuring clarity and consistency in our processes.
- Stay abreast of industry best practices and emerging technologies in data analytics, continuously expanding your knowledge and skills to drive ongoing improvement.
Requirements
- Degree in a quantitative discipline such as Statistics, Mathematics, Economics, Computer Science, Engineering, or related field.
- Minimum 2 years of experience in data analytics, preferably in fraud detection or financial services.
- Proficiency in SQL and Python for data manipulation and analysis.
- Experience with machine learning techniques and model development.
- Strong analytical and problem-solving skills with attention to detail.
- Excellent communication and presentation skills to convey complex findings to non-technical stakeholders.
- Ability to work independently and collaboratively in a fast-paced environment.
- Knowledge of fraud detection systems and rule engines is a plus.
- Experience with data visualization tools such as Tableau or Power BI is preferred.