Experience Inc. Jobs

Description

We are seeking a talented Business Intelligence Analyst II to contribute to building and maintaining our data environment, driving insights, and supporting the execution of fraud detection rules.

Key Responsibilities:

• Build and maintain underlying data sets to support the development of new fraud strategies and real-time detection rules.

• Contribute to the creation and maintenance of reusable data pipelines, focusing on feature creation and dataset design for fraud detection rule performance.

• Assist in developing the data environment needed for fraud detection and KPI reporting, including defining and calculating key performance metrics.

• Design datasets and dashboards for analyzing and monitoring new fraud detection rule performance and establishing alerting thresholds.

• Define strategies for monitoring key components of data pipelines, proactively detecting issues that may impact critical processes.

  Requirements

Requirements:

• Strong understanding of data modeling principles and experience with data integration and ETL (Extract, Transform, Load) processes.

• Proficiency in SQL for querying databases and creating curated datasets for reporting and analytics.

• Hands-on experience using BigQuery and expertise in designing interactive dashboards using Looker and Tableau.

• Experience and proficiency with GitHub for version control.

• Strong analytical skills to interpret data, derive insights, and define key performance indicators (KPIs).

• Understanding of retail banking, with a background in fraud or risk management considered ideal.

• Familiarity with data models used in financial services organizations is a plus.

• 2+ years of programming knowledge and experience with Python, SQL, SCALA, and ANVIL.

Preferred Skills:

• Ability to develop dashboards for monitoring fraud control performance, including account opening rule performance, debit authorization rule performance, and offline transaction detection processes.

• Experience defining and calculating performance metrics for fraud detection and establishing thresholds for alerting when metrics deviate from expected performance bounds.

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