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Assurance & Risk Analytics Specialist

We're partnering with a leading regional financial institution to hire a Data Analytics Specialist to focus on technology risk domain. This role is a great opportunity for someone passionate about data-driven risk insights, with a strong interest in analytics innovation and regulatory compliance.

You'll be joining a high-impact team responsible for enhancing risk visibility and automating assurance processes across complex tech and operational environments. This is a unique chance to make your mark on risk governance strategy and data-centric decision-making at scale.

Key Responsibilities

• Design, build, and maintain dashboards, data models, and analytics use cases to support risk assurance reviews and ongoing monitoring.
• Automate assurance testing workflows to improve efficiency, accuracy, and review coverage.
• Work with structured and unstructured data from various systems (e.g., audit logs, metrics, incidents) to power centralized insights.
• Use statistical and machine learning techniques to surface meaningful trends, anomalies, and risk signals.
• Ensure high standards in data quality, integrity, and governance, including data validation, transformation, and documentation.
• Collaborate across regions to champion data analytics initiatives that support broader risk and assurance programs.
• Stay up to date with the latest analytics trends and tools and contribute to the adoption of best practices.

Requirements:

• Degree in Data Analytics, Computer Science, Information Systems, or related field; Master's degree is a strong advantage.
• At least 8 years' experience in data analytics, ideally with exposure to technology risk, internal audit, or compliance functions in financial services.
• Strong grasp of application architecture, data visualization, and data transformation best practices.
• Technical proficiency in:
• Languages: Python, R, SQL, SAS
• Platforms: Cloudera, Hive, Hadoop, Spark
• Visualization tools: Power BI, Qlik
• Automation tools: RPA or similar technologies
• Experience working with predictive modeling and statistical techniques like clustering, regression, etc.
• A sharp, analytical mind with the curiosity to explore complex data sets and uncover insights that influence key decisions.
• Effective communication skills to translate technical findings into business-relevant insights for senior stakeholders.

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