Senior Digital Engineer – MBSE & Data Science

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.

Required Qualifications

• U.S. Citizen with an active TS/SCI security clearance and ability to maintain required access throughout employment.
• Bachelors 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.

Desired Qualifications

• 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.
• Masters 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.

$130000$195000 base salary annual bonus eligibility medical/dental/vision/STD/LTD/Life 401(k) PTO

Equal Employment Opportunity / Legal Disclaimer

GXM Technologies LLC is an Equal Opportunity Employer and participates in E-Verify to confirm employment eligibility. All qualified applicants will receive consideration for employment without regard to race color religion sex (including pregnancy) sexual orientation gender identity national origin age disability genetic information veteran status or any other legally protected status. GXM Technologies LLC provides reasonable accommodations in accordance with applicable law. This job description is not intended to be a complete list of duties and responsibilities which may change at any time with or without notice. Employment is at-will where permitted by law meaning either the employee or the Company may terminate employment at any time with or without cause or notice subject to applicable legal requirements.