Data Scientist - Active Secret Clearance Required

Summary

Modern Technology Solutions, Inc. (MTSI) is seeking a Data Scientist to support the USAF Test Pilot School at Edwards Air Force Base. The role leads data analysis for Doolittle Labs, establishes data-driven test infrastructure, supports flight-test planning and analysis, evaluates AI-agent performance, and communicates results to technical and leadership stakeholders.

Responsibilities

• Establish a “data-driven test” infrastructure and process for Doolittle Labs, aligned with the AFTC Multi-Domain Test Force (MDTF) and AFTC Data Working Group and VAULTIS-compliant for integration into the DAF Data Mesh Environment (DME)
• Support all flight-test activities executed by Doolittle Labs, including modeling and simulation (M&S), test-data planning, and data acquisition requirements definition
• Support flight-test data analysis post-event, including data received from OEM partners and translation of OEM outputs into Doolittle Labs analysis products
• Apply data-analysis tools to quantify AI-agent performance early—in simulation, HITL/SITL, and pre-flight environments; before flight test on the X-62A VISTA
• Provide and disseminate flight-test results to Doolittle Labs leadership, sponsors, PAE/PM reach-back customers, and, where and when authorized and cleared, the broader T&E community
• Ensure that task related products are consistent in format and content with overall project deliverables - Routinely engage with Govt technical representative + tech leads for our teammates and subcontractors
• Acts as a resource/mentor for colleagues with less experience
• Works independently, with guidance in only the most complex situations

Skills

• Deep knowledge of statistical methods, machine learning, and modern data-science techniques; such as regression, classification, clustering, deep learning, time-series analysis, and uncertainty quantification; as applied to research and development, engineering, and flight test data
• Knowledge of flight test methodologies, including test-plan development, test-point matrix construction, telemetry acquisition, post-flight reconstruction, and safety-of-flight considerations
• Familiarity with AI/autonomy T&E challenges, including verification and validation (V&V) of nondeterministic systems, human-agent trust calibration, demonstration of operational AI systems, and multi-agent scenarios
• Familiarity with the DAF Data Strategy (VAULTIS, DME, Domain Principals), the DoD Cyber Workforce Framework (DCWF) data work roles and KSATs, and applicable AFTC and AFMC data governance
• Knowledge of applicable federal and DoD policies governing controlled unclassified information (CUI), classified test data, intellectual property, and OEM data-rights posture (IAW DoW IP Guide)
• Proficiency with Python (e.g., pandas, NumPy, scikit-learn, PyTorch, TensorFlow) and at least one additional statistical/scientific computing environment (R, MATLAB, Julia)
• Experience with modern data engineering (SQL, cloud-native data warehouses, Parquet/Arrow, ETL pipelines) sufficient to design and build the Doolittle Labs data spine
• Experience with modeling-and-simulation environments used in flight test (MATLAB/Simulink, JSBSim, HITL/SITL benches) and their integration with data-analysis workflows
• Experience with visualization and results communication (Tableau, Power BI, matplotlib, plotly) sufficient to produce products for both engineering and leadership audiences
• Experience with version control, reproducible research, and MLOps practices (Git, Jupyter, MLflow or equivalent, model registries)
• Skill in translating ambiguous or evolving flight-test objectives into rigorous, quantifiable analysis plans and executable pipelines
• Minimum of 3-5 years of professional data-science experience, including demonstrable flight-test data analysis experience
• M.S. in Computer Science, Statistics, Data Science, Aerospace or Systems Engineering, Applied Mathematics, or a related STEM discipline (B.S. plus three additional years of qualifying experience may substitute)
• This position will require up to 40% to Edwards AFB, CA
• Active Secret clearance is required
• Ability to qualify for and maintain TS/SCI clearance
• U.S. Citizenship is required
• Familiarity with recent X-62 flight test campaigns (e.g., HAVE HEAT, DARPA ACE, HAVE HOLIDAYS) is a plus
• Prior experience at AFTC, USAF Test Pilot School (TPS), TRMC, AFRL, DARPA, or an OEM flight-test organization
• Experience with X-62A VISTA, F-16 VENOM, Group 1-3 UAS, or similar AI-in-the-loop flight-test programs
• Familiarity with VAULTIS-compliant data products or DAF Data Mesh Environment integration
• Publication record in flight-test T&E, AI evaluation, or related peer-reviewed venues
• Familiarity with VAULTIS / DAF Data Mesh Environment data-product concepts
• Within driving distance of a SCIF preferred

Qualifications

Must Haves

• Deep knowledge of statistical methods, machine learning, and modern data-science techniques; such as regression, classification, clustering, deep learning, time-series analysis, and uncertainty quantification; as applied to research and development, engineering, and flight test data
• Knowledge of flight test methodologies, including test-plan development, test-point matrix construction, telemetry acquisition, post-flight reconstruction, and safety-of-flight considerations
• Familiarity with AI/autonomy T&E challenges, including verification and validation (V&V) of nondeterministic systems, human-agent trust calibration, demonstration of operational AI systems, and multi-agent scenarios
• Familiarity with the DAF Data Strategy (VAULTIS, DME, Domain Principals), the DoD Cyber Workforce Framework (DCWF) data work roles and KSATs, and applicable AFTC and AFMC data governance
• Knowledge of applicable federal and DoD policies governing controlled unclassified information (CUI), classified test data, intellectual property, and OEM data-rights posture (IAW DoW IP Guide)
• Proficiency with Python (e.g., pandas, NumPy, scikit-learn, PyTorch, TensorFlow) and at least one additional statistical/scientific computing environment (R, MATLAB, Julia)
• Experience with modern data engineering (SQL, cloud-native data warehouses, Parquet/Arrow, ETL pipelines) sufficient to design and build the Doolittle Labs data spine
• Experience with modeling-and-simulation environments used in flight test (MATLAB/Simulink, JSBSim, HITL/SITL benches) and their integration with data-analysis workflows
• Experience with visualization and results communication (Tableau, Power BI, matplotlib, plotly) sufficient to produce products for both engineering and leadership audiences
• Experience with version control, reproducible research, and MLOps practices (Git, Jupyter, MLflow or equivalent, model registries)
• Skill in translating ambiguous or evolving flight-test objectives into rigorous, quantifiable analysis plans and executable pipelines
• Minimum of 3-5 years of professional data-science experience, including demonstrable flight-test data analysis experience
• M.S. in Computer Science, Statistics, Data Science, Aerospace or Systems Engineering, Applied Mathematics, or a related STEM discipline (B.S. plus three additional years of qualifying experience may substitute)
• This position will require up to 40% to Edwards AFB, CA
• Active Secret clearance is required
• Ability to qualify for and maintain TS/SCI clearance
• U.S. Citizenship is required

Nice to Haves

• Familiarity with recent X-62 flight test campaigns (e.g., HAVE HEAT, DARPA ACE, HAVE HOLIDAYS) is a plus
• Prior experience at AFTC, USAF Test Pilot School (TPS), TRMC, AFRL, DARPA, or an OEM flight-test organization
• Experience with X-62A VISTA, F-16 VENOM, Group 1-3 UAS, or similar AI-in-the-loop flight-test programs
• Familiarity with VAULTIS-compliant data products or DAF Data Mesh Environment integration
• Publication record in flight-test T&E, AI evaluation, or related peer-reviewed venues
• Familiarity with VAULTIS / DAF Data Mesh Environment data-product concepts
• Within driving distance of a SCIF preferred

Benefits

• MTSI also offers a full range of medical, financial, and other benefits, dependent on the position offered.