Lead Data Scientist

The Mitre Corporation•Huntsville, AL

13d•$155,200 - $232,800•Onsite

About The Position

MITRE’s Systems Engineering Analytics department seeks a Lead Data Scientist to apply data science, machine learning, AI, and data engineering to problems of national importance. MITRE is a nonprofit engineering, applied research, and advanced technology organization that works in the public interest and leads federally funded research and development centers for U.S. government agencies. The role offers opportunities across national security, intelligence, transportation, and healthcare, as well as potential participation in MITRE’s internal research and development program. The successful candidate will combine expertise in statistical analysis, machine learning, AI, and software development with hands-on experience preparing and analyzing complex data, developing and evaluating models, and delivering analytic capabilities for mission problems. They will help government sponsors develop and evaluate technical approaches and implementation plans for adopting data-driven capabilities across the national security community. The role contributes to data and analytics strategies, assesses the suitability of data, models, and architectures for mission applications, and provides evidence-based decision support. The Lead Data Scientist will serve as a trusted technical advisor and may be a technical point of contact for government sponsors. They will create clear and actionable deliverables, communicate technical concepts and findings to decision makers, align data science and AI capabilities with mission needs, lead technical efforts, mentor colleagues, and collaborate with engineering teams to transition analytical approaches into operational capabilities. The position requires at least four days per week on-site. An active Secret U.S. government-issued clearance is required, and the candidate must be eligible to obtain and maintain a Top Secret/SCI clearance. U.S. citizenship is required for security-clearance eligibility.

Requirements

Bachelor’s degree in computer science, Data Science, Statistics, Applied Mathematics, Operations Research, Information Systems, or a related field and 8 years of relevant experience; or a master’s degree in a related field and 6 years of relevant experience; or a PhD in a related field and 3 years of relevant experience.
Hands-on experience applying statistical analysis and machine learning techniques to real-world problems, including developing, validating, and evaluating models or analytic solutions.
Programming proficiency in Python or R and experience using SQL to access, prepare, and analyze data.
Experience working with complex datasets and developing reproducible data preparation, analysis, and modeling workflows.
Ability to rapidly understand unfamiliar mission problems and translate them into analytical questions and technical approaches.
Strong systems thinking and analytical problem-solving skills.
Experience integrating commercial, government, and open-source data or technologies to solve operational problems.
Ability to analyze complex technical information and clearly communicate findings, limitations, and recommendations to technical and non-technical audiences.
Strong written and verbal communication skills.
Experience collaborating with multidisciplinary teams and stakeholders and leading technical tasks or analytic efforts.
Active Secret U.S. government-issued security clearance and eligibility to obtain and maintain a Top Secret/SCI U.S. government-issued security clearance.
U.S. citizenship, as required for security-clearance eligibility.
Availability to work on-site a minimum of 4 days a week.

Nice To Haves

Master’s degree or PhD in Computer Science, Data Science, Statistics, Applied Mathematics, Operations Research, Information Systems, or a related field.
Active DoD Top Secret/SCI clearance.
Prior experience as a Data Scientist, Applied Scientist, Machine Learning Engineer, or Data Engineer supporting U.S. government organizations.
Knowledge of military intelligence, operations, or logistics and related data processes and systems used or relied upon by Combatant Commands.
Recent experience with multi-domain data, including publicly available information, commercially available information, traditional intelligence, and operational data, and its preparation, integration, analysis, modeling, and visualization.
Experience developing and evaluating advanced machine learning or AI capabilities, including foundation model integration, retrieval-augmented generation, domain adaptation, or multimodal analysis.
Experience with advanced data architectures and scalable analytical environments, including lakehouse platforms, distributed processing, vector databases, and cloud, hybrid, or restricted-network deployments.
Experience transitioning analytic prototypes or machine learning models into operational use, including reproducibility, deployment evaluation, performance monitoring, and collaboration with engineering teams.
Experience applying data quality, governance, responsible AI, and data or model provenance practices to support trustworthy analytic outputs.
Experience mentoring technical staff, reviewing analytical methods and software, and guiding multidisciplinary teams through complex data science efforts.
Ability to operate independently in a sponsor-facing environment with minimal supervision.

Responsibilities

Translate complex mission needs into analytical questions, measurable objectives, and implementable technical approaches.
Apply statistical inference, experimental design, causal inference, or simulation to support defensible recommendations.
Develop and evaluate statistical, machine learning, and deep learning models for applications such as prediction, classification, forecasting, anomaly detection, natural language processing, computer vision, graph analytics, and multimodal analysis.
Prepare, integrate, and engineer features from large, heterogeneous datasets, addressing issues such as entity resolution, schema harmonization, missing data, class imbalance, noisy labels, and limited training data.
Design rigorous model validation approaches, including baseline comparisons, cross-validation, temporal and out-of-distribution testing, hyperparameter optimization, uncertainty assessment, and prevention of data leakage.
Select, adapt, and integrate foundation models, including large language and multimodal models, using prompting, retrieval-augmented generation, fine-tuning, or transfer learning, and connect them to enterprise data and tools.
Develop AI evaluation datasets and benchmarks to assess accuracy, grounding, hallucinations, robustness, and operational suitability, incorporating interpretability, provenance, responsible AI practices, and appropriate human oversight.
Design and assess analytics-ready data architectures, including warehouses, lakes, lakehouse platforms, and relational, NoSQL, graph, and vector databases, with supporting metadata, semantic models, lineage, governance, and access controls.
Develop scalable batch and streaming data pipelines using distributed processing and workflow orchestration; assess compute, storage, security, performance, and cost tradeoffs across cloud, on-premises, hybrid, or restricted-network environments.
Develop maintainable, tested, and reproducible analytic software using Python, SQL, and relevant frameworks, supported by feature pipelines, experiment tracking, model registries, and data, code, and model versioning.
Transition analytic prototypes and AI models into operational capabilities with engineering teams, including deployment readiness, integration, performance monitoring, drift detection, retraining, and rollback.
Lead technical teams, analytical workstreams, or projects with moderate risk and resource requirements.
Provide in-depth technical expertise, identify and resolve complex analytical problems, and develop practical solutions that improve mission outcomes.
Communicate technical concepts, tradeoffs, limitations, findings, and recommendations to technical and non-technical audiences and influence stakeholders toward project and mission objectives.
Mentor colleagues and transfer knowledge through technical review and collaboration.