Senior Digital Engineer – MBSE & Data Science

GXM Technologies LLC•Colorado Springs, CO

2d•$130,000 - $195,000•Onsite

About The Position

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.

Requirements

U.S. Citizen with an active TS/SCI security clearance and ability to maintain required access throughout employment.
Bachelor’s degree in Systems Engineering, Engineering, Computer Science, Data Science, Applied Mathematics, Statistics, Operations Research, Information Systems, or a related technical or quantitative discipline.
5+ years of relevant experience in systems engineering, digital engineering, MBSE, mission engineering, data science, analytics, or architecture development, including demonstrated experience working across multiple disciplines.
Hands-on experience with MBSE tools such as Cameo Systems Modeler/MagicDraw, Sparx Enterprise Architect, or comparable modeling platforms.
Experience developing SysML-based architecture models, engineering artifacts, requirements relationships, interface definitions, system dependencies, and technical documentation.
Demonstrated data analysis or data science experience using Python and SQL, including data manipulation, exploratory analysis, statistics, and visualization.
Working knowledge of Python data-science libraries and analytic environments such as pandas, NumPy, SciPy, scikit-learn, Matplotlib/Plotly, Jupyter, or equivalent tools.
Experience translating mission, operational, or engineering questions into measurable analytic approaches, identifying appropriate data, defining assumptions, and communicating limitations and results.
Familiarity with data modeling, data pipelines, APIs/interfaces, structured and semi-structured data, metadata, data quality, lineage, and provenance concepts.
Working knowledge of statistical methods and machine learning fundamentals, including model selection, validation, performance metrics, overfitting, uncertainty, and appropriate use of training/test data.
Experience supporting requirements management, traceability, technical baseline development, configuration management, and engineering change assessment.
Experience working within Agile, DevSecOps, or other iterative engineering and software-delivery environments.
Strong analytical reasoning, problem-solving, technical writing, communication, and stakeholder-engagement skills.

Nice To Haves

Experience supporting defense, space, intelligence, homeland defense, or multi-domain operational environments, particularly Command and Control (C2), Space Domain Awareness (SDA), mission systems, or enterprise modernization initiatives.
Experience applying the DoD Digital Engineering Strategy, digital-thread concepts, mission engineering, DoDAF/UAF, or SysML-based architecture development in a DoD environment.
Experience with Cameo Teamwork Cloud, model repositories, collaborative model governance, model validation, or integration of MBSE tools with requirements and lifecycle-management platforms.
Experience developing or evaluating AI/ML-enabled data fusion, anomaly detection, predictive analytics, sensor/data correlation, decision-support analytics, or other operational analytics for mission environments.
Experience with cloud-native or distributed data environments, data engineering platforms, containerized analytics, APIs, message/event data, or big-data technologies in secure environments.
Familiarity with data engineering and MLOps concepts, including version control, reproducible pipelines, model/data versioning, test automation, monitoring, and deployment within DevSecOps environments.
Master’s degree in Systems Engineering, Data Science, Computer Science, Applied Mathematics, Statistics, Operations Research, or a related technical field.
OCSMP, INCOSE ASEP/CSEP/ESEP, Cameo certification, cloud/data engineering certification, or recognized data science/AI certification.

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.