Data Analyst IV

Systems Innovation Engineering

About The Position

The Data Analyst IV serves as a recognized subject matter expert and advanced individual contributor, leading complex data analytics and statistical modeling efforts to develop readiness based spares to drive strategic and operational decision-making across the organization. This role translates high-level business challenges into advanced analytical frameworks, develops predictive and diagnostic models, and delivers compelling data narratives to executive stakeholders. The position requires a highly specialized skill set, extensive experience, and full autonomy in executing highly complex assignments, with significant latitude for independent judgment. This role works closely with subject matter experts in data engineering, system engineering, technical leadership and analytical expertise. This position is responsible for developing and executing complex readiness, sustainment, and logistics modeling efforts in direct support of Navy and Department of War (DoW) programs. Integrates multidisciplinary engineering methodologies to ensure that model development, data management, and analytical outputs align with program objectives, technical standards, and customer requirements. The ideal candidate has 10+ years of experience assessing data and Opus Suite.

Requirements

Bachelor’s or Master’s degree in Engineering, Computer Science, Data Analytics, or a related technical field.
Minimum of 10+ years of experience in systems engineering, logistics analysis, or modeling and simulation
Advanced experience in data engineering practices and readiness modeling methodologies within the DoD or equivalent complex enterprise environment.
Expert in the application of reliability, maintainability, and availability (RMA) principles and lifecycle sustainment analysis.
Lead multidisciplinary teams and coordinating across multiple stakeholders to deliver integrated technical solutions.
Strong communication, leadership, and analytical problem-solving skills.

Nice To Haves

Expert with Opus Suite (OPUS10, SIMLOX, CONNECT) or equivalent readiness-based sparing platforms.
Experience exporting and analyzing data from Navy authoritative systems
Familiar with development of supportability analysis
Familiar with a developing a readiness based spares package
Understanding of the aerospace and defense industry.
Knowledge of DoW policies and requirements

Responsibilities

Lead advanced data analytics, statistical modeling, and predictive analysis initiatives to support readiness based sparing models to support decision-making and strategic planning.
Partner with senior stakeholders to define analytical objectives, shape problem statements, and translate business challenges into actionable, data-driven strategies.
Enforce, maintain and contribute process improvements to documented enterprise data definitions, business rules, governance frameworks, and data quality standards.
Provide technical leadership and mentorship to junior analysts, supporting skill development and analytical best practices.
Serve as a trusted advisor and subject matter expert on data analytics methodologies, tools, and advanced analytical techniques.
Contribute technical expertise in data needed and structure to develop and model the simulation to identify the most effective option on the readiness cost curve.
Participate in planning sessions to acquire detailed information develop data pipeline architecture that align with project requirements.
Extract supporting data from identified tools, review available supportability reports and engineering details to assess quality of data prior to developing a readiness at cost model.
Document data gathering processes, pipeline configurations, and data flows to ensure consistency and future support.
Identify, define, and lead continuous improvement initiatives for analytics processes, data workflows, and system performance.
Ensure accuracy and completeness of all documentation.
Foster effective collaboration with cross-functional teams to achieve project objectives.
Communicate complex technical information clearly and effectively.
Utilize identified software and follow business rules and processes.
Identify areas to improve efficiency and accuracy and integrate those into modeling best practices