Vice President, Technology Management - Cybersecurity

Job Description:

Job Description - Head of Detection Analytics
Mandate: The Head of Detection Analytics is responsible for leading and overseeing all elements of the detection analytics vertical. This role involves leveraging advanced analytics, machine learning, and data-driven insights to proactively identify and mitigate fraud risks. The Head of Detection Analytics ensures that fraud detection strategies are both proactive and responsive to emerging threats and business needs. This role also involves aligning detection efforts with business units, products, and channels, and collaborating closely with fraud operations to optimize alerting strategies and capacity planning.

Key Responsibilities:

Strategic Leadership:

• Develop and implement the overall detection analytics strategy.
• Align detection efforts with the organization's broader fraud risk management framework.
• Provide strategic direction and leadership to the detection analytics team.

Risk Engine Rule Management:

• Oversee the development and optimization of risk engine rules.
• Ensure rules are continuously updated based on new data and insights.
• Collaborate with other teams to align rules with overall fraud prevention strategies.

Model Development and Integration:

• Define requirements for new fraud detection models.
• Work with data scientists to develop and test models.
• Ensure models are effectively integrated into the fraud detection system.

Decision Support:

• Ensure models are used to assist with fraud detection decisions.
• Train analysts on how to use models for decision-making.
• Monitor model performance and provide feedback for improvements.

Strategy Calibration:

• Calibrate fraud detection strategies based on signals received, such as fraud pressure or business needs.
• Monitor signals and adjust strategies accordingly.
• Document and communicate strategy changes to relevant stakeholders.

Transforming Risk Appetite into Strategies:

• Develop fraud detection strategies based on the organization's risk appetite.
• Monitor and assess the effectiveness of strategies.
• Adjust strategies as needed to address emerging risks.

Reporting and Dashboard Requirements:

• Develop and produce ad hoc reports on fraud detection activities.
• Define requirements for fraud detection dashboards.
• Ensure dashboards provide relevant and actionable insights.

Capacity Planning and Alerting Strategies:

• Collaborate with fraud operations to negotiate capacity plans.
• Develop and implement alerting strategies that optimize resource use.
• Monitor and adjust alerting strategies based on capacity and fraud pressure.

Business Unit, Product, and Channel Alignment:

• Align fraud detection efforts with the needs of different business units, products, and channels.
• Develop and implement tailored fraud detection strategies.
• Monitor and assess the effectiveness of aligned strategies.

Team Leadership and Development:

• Lead and mentor the detection analytics team, ensuring they are well-trained and equipped to handle fraud risks.
• Promote a culture of continuous learning and improvement within the team.
• Manage team performance and provide regular feedback and development opportunities.

Collaboration and Communication:

• Foster strong relationships with other teams, including fraud operations and business units.
• Ensure effective communication and collaboration across the organization.
• Provide regular updates and reports to senior management on detection analytics activities and outcomes.

By fulfilling these responsibilities, the Head of Detection Analytics ensures that the organization remains proactive and resilient against fraud threats, leveraging advanced analytics and data-driven insights to protect customers and assets.

Experience:

Extensive Experience in Analytics and Fraud Detection:

• At least 7-10 years of experience in analytics, data science, or a related field.
• Proven track record of developing and implementing fraud detection strategies using advanced analytics and machine learning.

Leadership Experience:

• Significant experience in leading and managing analytics or data science teams.
• Demonstrated ability to mentor and develop team members.

Industry Knowledge:

• Experience in relevant industries such as banking, finance, e-commerce, or telecommunications.
• Strong understanding of industry-specific fraud risks and regulatory requiremen