Role Purpose
Is responsible for driving analysis leading towards data-driven decision-making, optimizing airspace operations, and enhancing regulatory processes through advanced analytics and artificial intelligence. This role focuses on developing and implementing AI-driven models, predictive analytics, and machine learning solutions to support airspace management, safety oversight, and unmanned traffic management (UTM).
Key Responsibilities/Duties
1. Strategic Data Analysis & Interpretation: Lead the Safety Data Collection & Processing System (SDCPS) collection, processing, and advanced analysis related to aviation operations, safety, compliance, airspace management, and unmanned traffic management (UTM). Deliver strategic insights to senior leadership, enabling data-driven policy formulation and operational enhancements.
2. AI & Machine Learning Innovation: Design, develop, and implement AI-driven models and machine learning algorithms to support aviation regulatory functions, including predictive risk assessment, safety oversight, airspace optimization, and real-time UTM monitoring. Continuously enhance AI models to align with evolving industry demands and regulatory frameworks.
3. Advanced Predictive Analytics & Risk Management: Utilize advanced predictive analytics to forecast industry trends, detect emerging risks, and propose proactive mitigation strategies. Drive the development of AI-powered decision support systems for airspace planning, UTM integration, and operational resilience. Utilization of internally recognized methodologies such as ADREP and ECCAIRS.
4. Regulatory Strategy & AI Integration: Provide expert guidance on embedding AI and big data analytics into regulatory frameworks, ensuring compliance with international aviation standards. Lead initiatives that leverage AI to optimize regulatory processes, including automated certification, surveillance, incident analysis and Safety Intelligence aspects.
5. Airspace Management & UTM Strategy: Spearhead the development of AI-driven airspace management solutions, enabling dynamic airspace allocation, automated conflict resolution, and seamless UAS integration. Collaborate with policymakers and industry leaders to shape the future of UTM and next-generation airspace infrastructure.
6. Cross-Sector Collaboration & Stakeholder Engagement: Act as a key liaison between regulatory authorities, air navigation service providers, UAS operators, and technology firms to drive data-sharing initiatives, foster innovation, and enhance airspace safety through AI applications. Represent the organization in global aviation and AI forums.
7. Continuous Innovation & Industry Leadership: Stay at the forefront of emerging technologies in AI, machine learning, and data analytics, particularly as they apply to aviation, airspace management, and UTM. Champion the adoption of cutting-edge methodologies and best practices to maintain a leading edge in regulatory technology.
8. Governance, Compliance & Ethical AI: Ensure AI solutions adhere to ethical standards, regulatory mandates, and cybersecurity best practices. Advocate for transparency, fairness, and accountability in AI applications, driving policy recommendations for responsible AI deployment in aviation.
9. Executive-Level Reporting & Data Visualization: Develop and present high-impact data visualizations, dashboards, and AI-driven insights tailored for senior executives and policymakers. Translate complex analytical findings into actionable strategies that support decision-making at the highest levels.
10. Mentorship & Capacity Building: Lead knowledge-sharing initiatives, mentor junior analysts, and develop training programs to enhance AI and data analytics capabilities within the organization. Drive a culture of continuous learning and technical excellence in aviation analytics.
- Ensure compliance with non-disclosure and confidentiality agreements, as well as DCAA security policies, to safeguard both government and DCAA confidential information. Address and rectify any ISR audit findings, promptly report security incidents, and engage in ongoing security awareness training to prevent unauthorized access or disclosure.
Academic Qualifications:
Bachelor’s degree in Data Science, Computer Science, Aviation Management, or a related field. Preference will be given to holders of postgraduate qualification.
Certifications:
Professional Experience: