GXM is seeking a Senior Digital Engineer – MBSE & Data Science to support advanced defense and space-related mission programs focused on Command and Control (C2), mission systems integration, cloud modernization, data-driven decision support, and enterprise capability delivery.
The selected candidate will combine Digital Engineering and Model-Based Systems Engineering (MBSE) with data science and analytics to develop and maintain authoritative digital representations of mission systems and their operational context. The role will connect mission threads, operational workflows, requirements, system architectures, interfaces, data flows, analytics, and technical baselines into a traceable digital engineering environment that supports integration, assessment, and decision-making across the system lifecycle.
The candidate will use engineering models and mission data to characterize system dependencies, assess integration and operational performance, identify capability and data gaps, support technical trade studies, and evaluate analytic or AI/ML-enabled capabilities. As appropriate, the candidate will develop repeatable analysis workflows using Python, SQL, Jupyter, statistical methods, data visualization, and machine learning techniques to inform architecture and mission-engineering decisions.
This role requires close collaboration with enterprise and solutions architects, systems engineers, software and data engineers, cybersecurity personnel, mission operators, and Government stakeholders to ensure engineering models, data relationships, analytic assumptions, and technical decisions are accurate, explainable, traceable, and aligned to mission outcomes.
This position is onsite in Colorado Springs, CO. Hybrid flexibility may be available over time based on mission requirements, classified work requirements, program execution needs, and achievement of objectives.
Responsibilities
- Develop, maintain, and govern MBSE models supporting mission systems, enterprise capabilities, operational architectures, and C2 integration using SysML and related digital engineering methods.
- Model mission threads, operational workflows, system functions, interfaces, dependencies, data exchanges, analytic services, and decision-support relationships to provide an integrated view of mission execution and system behavior.
- Establish and maintain digital-thread traceability from mission needs and operational use cases through requirements, architecture elements, interfaces, data sources, analytic functions, verification evidence, and mission outcomes.
- Develop and maintain data architecture artifacts, including logical and physical data flows, source-to-consumer mappings, data/interface relationships, schemas, metadata, data lineage, and provenance needed to support integration and analytics.
- Acquire, clean, transform, explore, and analyze structured and unstructured data to support engineering analysis, mission assessment, capability evaluation, and operational decision support.
- Apply statistical analysis, feature engineering, anomaly detection, classification, clustering, forecasting, or other machine learning techniques when appropriate; select methods based on mission need, data characteristics, and operational constraints rather than technology novelty.
- Evaluate analytic and AI/ML-enabled capabilities using mission-relevant measures of performance and effectiveness, including accuracy, precision/recall, latency, confidence, robustness, uncertainty, false-alarm rates, and operational utility as applicable.
- Support explainable and auditable AI/ML integration by maintaining traceability to source data, data transformations, model versions, analytic methods, assumptions, confidence measures, provenance, and operator actions.
- Assess data quality, completeness, consistency, timeliness, latency, availability, and fitness for use; identify data risks and recommend engineering or operational mitigations.
- Create clear technical visualizations, engineering views, analytic products, and decision-support artifacts that communicate system behavior, integration dependencies, data relationships, technical risks, and mission impact to technical and non-technical stakeholders.
- Support requirements engineering activities, including elicitation, decomposition, allocation, validation, verification planning, change impact analysis, and requirements-to-architecture traceability.
- Conduct model- and data-informed trade studies, sensitivity analyses, gap assessments, and technical evaluations to support architecture decisions, capability insertion, integration planning, and technical baseline management.
- Support development and management of technical baselines across hardware, software, data, infrastructure, cloud, security, and operational environments.
- Participate in architecture reviews, engineering working groups, technical assessments, model governance activities, configuration management, and design decisions; ensure digital engineering artifacts remain synchronized with implemented system changes.
- Collaborate with Agile and DevSecOps teams to integrate engineering models, requirements, data products, analytic prototypes, interfaces, and verification evidence into iterative capability releases.