US citizens onlyActive Secret clearance is required Ability to qualify for and maintain TS/SCI clearance US Citizenship is required The expected pay range for this position in California is $XXX,000/year to $XXX,000/year
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pandas
Plotly
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scikit-learn
TensorFlow
Where the posting names them
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 modeling-and-simulation environments used in flight test (MATLAB/Simulink, JSBSim, HITL/SITL benches) and their integration with data-analysis workflows.
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 version control, reproducible research, and MLOps practices (Git, Jupyter, MLflow or equivalent, model registries).
Experience with visualization and results communication (Tableau, Power BI, matplotlib, plotly) sufficient to produce products for both engineering and leadership audiences.
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About the role
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
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 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 recent X-62 flight test campaigns (e.g., HAVE HEAT, DARPA ACE, HAVE HOLIDAYS) is a plus. 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. Collaboration/Teamwork: Engages others across roles through communication and mutual respect, shares insights, enabling effective teamwork to ensure collective success. Entrepreneurialism: Fosters innovation, evaluates ideas, and advances initiatives with sound judgment and organizational awareness to drive growth and impact. (innovation) Communication: Communicates clearly and effectively, fostering understanding, collaboration, and alignment through active listening and impactful messaging. (effective communication) Customer Focus: Prioritizes customer needs, builds trust, and delivers exceptional service by using insights to drive improvements and strengthen relationships. (customer insight) Minimum of 3-5 years of professional data-science experience, including demonstrable flight-test data analysis experience. 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. M.S. in Computer Science, Statistics, Data Science, Aerospace or Systems Engineering, Applied Mathematics, or a related STEM discipline (B.S. plus eight additional years of qualifying experience may substitute). Active Secret clearance is required Ability to qualify for and maintain TS/SCI clearance U.S. Citizenship is required The expected pay range for this position in California is $XXX,000/year to $XXX,000/year; however, base pay offered may vary depending on established government contract ranges, job-related knowledge, skills, and experience, and other factors. Base pay information is based on market location. LI-RR1 mtsi LI-Onsite