Job Description & How to Apply Below
DESCRIPTION
Deputy Chief of Staff Capability Development (DCOS CAPDEV) acts as the Supreme Allied Commander Transformation's Director for guidance, direction and co-ordination of the activities and resources of the Capability Development Directorate. CAPDEV is responsible to:
ü Identify and prioritize Alliance capability development from short to long term, ensuring coherence between all capabilities within the CAPDEV portfolio.
ü Lead the determination of required capabilities and prioritization of shortfalls to inform the delivery of materiel and non-materiel solutions across the Doctrine, Organisation, Training, Material, and Leadership, Personnel, Facilities and Interoperability (DOTMLPFI) lines of effort to enable a holistic approach to capability development, ensuring improved interoperability, deployability and sustainability of Alliance Forces.
The future Capability Development Directorate will include enduring functionality to effectively plan and manage coherent through life capability development, aligned to NATO's strategic intent and priorities. The CAPDEV Data and Analytics Office (DAO) is responsible to DCOS for managing the data and platform operations for Capability Lifecycle, Requirements, and P3M data as well as providing analytics as service and enabling self-service analytics for CAPDEV decision makers.
As part of ongoing organisational functional reviews, CAPDEV is in the process of implementing measures for improved capability development planning and management, including the way it collects, manages, analyses and reports on capability development and delivery information, both legacy and current.
EXPERIENCE AND EDUCATION:
Essential Qualifications/Experience:
• 8+ years of progressive professional experience in data science, advanced analytics, and/or machine learning engineering, including experience delivering operational analytics or decision-support solutions in complex enterprise environments
• Demonstrated expertise in machine learning and statistical modeling, including development, training, validation, and deployment of models supporting forecasting, risk analysis, performance assessment, or decision support across business or capability life cycles
• Demonstrated experience designing and operating automated data pipelines, including ETL/ELT workflows, feature engineering, and data transformation processes to support analytics and AI/ML workloads
• Demonstrated professional experience with cloud-based analytics and AI/ML platforms, including deployment and operation of models and data pipelines in secure, scalable cloud environments
• Bachelor's degree in Data Science, Computer Science, Mathematics, Engineering, Statistics, or a related quantitative discipline
• Demonstrated experience integrating AI/ML solutions into enterprise analytics tools, dashboards, or reporting platforms to support operational use by analysts and decision-makers
• Demonstrated experience with model lifecycle management, including performance monitoring, retraining strategies, version control, documentation, and optimization for production environments
• Demonstrated experience working within governed or regulated environments, including adherence to data governance, security, and compliance requirements relevant to defence, security, or other highly regulated domains
• Demonstrated ability to collaborate across multidisciplinary teams, including analysts, data engineers, platform engineers, and system administrators, to deliver interoperable, production-ready analytics solutions
• Demonstrated ability to communicate complex analytical and AI/ML concepts clearly to both technical and non-technical stakeholders, supporting effective adoption and operational use of delivered solutions. Demonstrated minimum NATO or National SECRET clearance with the appropriate national authority for the duration of the contract
• Demonstrable proficiency in effective oral and written communication, including briefing and coordinating with business stakeholders
DUTIES/ROLE:
• AI/ML Model Development:
Design, develop, train, and deploy machine learning models to support forecasting, risk identification, readiness…
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