Data Modeler (Data Risk Analyst)

Title: Data Modeler (Data Risk Analyst)

Location : Florida Turnpike Enterprise HQ, Ocoee, FL (Onsite – 5 Days)

Roles & Responsibility Summary

This role leads the analysis, implementation, execution and on-going enhancements to business rules based on data analytic findings. Collaborates with the finance, technology, and business teams to ensure effective communication and alignment while identifying opportunities to improve controls and reduce risk.

Key Responsibilities:

• Develop and implement advanced statistical models and machine learning algorithms to solve complex financial problems.
• Analyze large and complex financial datasets to identify trends, patterns, and opportunities for increased controls and enhanced risk mitigation.
• Build predictive data models for risk assessment.
• Forecast, track, and measure key metrics.
• Collaborating with IT and finance teams to translate data insights into actionable strategies and initiatives to implement.
• Staying current on financial industry best practices and emerging risk factors.
• Conducting regular security audits and penetration testing.
• Analyzing security incidents and implementing corrective actions.

Requirements:

• Bachelor's degree in mathematics, computer science, engineering, statistics, fintech or related field.
• Minimum of 5 years of experience in Financial Services, with a strong focus on data intelligence and analysis.
• Proven expertise in risk assessment, data analysis technologies and tools.
• Strong analytical and problem-solving skills.
• Excellent communication and interpersonal skills.
• Ability to work independently and as part of a team.

Preferred Qualifications:

• Experience in financial services industry retail banking products
• Experience in data analysis and financial risk management
• Knowledge of forensic financial auditing techniques.
• Proficiency in scripting and programming languages (e.g., Python, Bash, SQL)
• Understanding of network security, encryption and access control principles.
• Knowledge of machine learning and data analytics.
• Relevant certifications such as CDA, CRMA or similar are highly desirable.