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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Data Scientist - **Company:** MITRE Corporation - **Location:** El Segundo, CA, United States - **Experience:** Expert - **Salary:** $155,200.0 - $194,000.0 - **Contract:** Permanent contract - **Skills:** Training Data, AI Evaluation, Artificial Intelligence, Business Analytics Applications, Data Analysis, Computer Vision, Big Data, Cloud Computing, Information Systems, Computer Programming, Data Architecture, Data Cleansing, Information Leak Prevention, Decision Support Systems, Distributed Computing Environment, Python (Programming Language), Machine Learning, Natural Language Processing, Network Installation Services, NoSQL, Operational Data Store, Software Engineering, SQL Databases, Data Streaming, Systems Integration, Management of Software Versions, Workflow Management Systems, Enterprise Data Management, Feature Engineering, Retrieval-Augmented Generation, Transfer Learning, Large Language Models, Deep Learning, Model Validation, Information Technology, Data Analytics, Performance Monitor, Drift Detection - **Published:** October 18, 2026 - **Apply:** https://dejobs.org/x/x/F3C08FA999E74C41A6E7DFD4F030A2B5/job/ ## About the Role * Bachelor's degree in computer science, Data Science, Statistics, Applied Mathematics, Operations Research, Information Systems, or a related field with 8 years of relevant experience, or a master's degree in a related field with 6 years of relevant experience, or a PhD in a related field with 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, with 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 communicate findings, limitations, and recommendations clearly 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. * Must have an active Secret U.S Government issued Security Clearance and must be eligible to obtain and maintain a Top Secret/SCI U.S Government issued Security Clearance. Per the U.S. Government's eligibility requirements, you must be a U.S Citizen to be considered for a security clearance. * This position requires a minimum of 4 days a week on-site, * 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 working 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 working 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. This requisition requires the candidate to have a minimum of the following clearance(s) ## Description The successful candidate for this position is a Data Scientist who combines strong foundations 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 to solve mission problems. Our organization values innovation and believes that keeping up with the latest research and technologies is essential. The Data Scientist will help our government sponsors develop and evaluate technical approaches and implementation plans supporting the adoption of data-driven capabilities across the national security community. The Data Scientist will contribute to data and analytics strategies, assess the suitability of data, models, and supporting architectures for mission applications, and provide evidence-based decision support. The successful candidate will serve as a trusted technical advisor and may act as a technical point of contact for government sponsors within assigned projects. The candidate will be expected to develop clear, concise, and actionable deliverables, communicate complex technical concepts and analytic findings to decision makers, and align data science and AI capabilities with mission needs. The candidate will also lead technical efforts, mentor colleagues, and collaborate with engineering teams to transition analytical approaches into operational capabilities. The candidate should have hands-on experience in many of the areas specified below: * Translation of complex mission needs into analytical questions, measurable objectives, and implementable technical approaches, including application of statistical inference, experimental design, causal inference, or simulation to support defensible recommendations. * Development and evaluation of statistical, machine learning, and deep learning models for applications such as prediction, classification, forecasting, anomaly detection, natural language processing, computer vision, graph analytics, or multimodal analysis. * Preparation, integration, and feature engineering of large, heterogeneous datasets, including entity resolution, schema harmonization, and approaches for addressing missing data, class imbalance, noisy labels, and limited training data. * Design of rigorous model validation approaches, including baseline comparisons, cross-validation, temporal and out-of-distribution testing, hyperparameter optimization, uncertainty assessment, and prevention of data leakage. * Selection, adaptation, and integration of foundation models, including large language models and multimodal models, using prompting, retrieval-augmented generation, fine-tuning, and transfer learning, with connections to enterprise data and tools. * Development of 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 assessment of 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. * Development of scalable batch and streaming data pipelines using distributed processing and workflow orchestration, including assessment of compute, storage, security, performance, and cost tradeoffs across cloud, on-premises, hybrid, or restricted-network environments. * Development of maintainable, tested, and reproducible analytic software using Python, SQL, and relevant frameworks, supported by feature pipelines, experiment tracking, model registries, and versioning of data, code, and models. * Transition of analytic prototypes and AI models into operational capabilities with engineering teams, including deployment readiness, integration, performance monitoring, drift detection, retraining, and rollback throughout the model lifecycle. Expectations for this position are as follows: * Leads technical teams, analytical workstreams, or projects with moderate risk and resource requirements. * Works independently, with guidance needed primarily in highly complex situations. * Provides in-depth technical expertise in data science, machine learning, and applied AI, including selection of appropriate methods, evaluation of results, and assessment of supporting data architectures. * Identifies and resolves complex analytical problems and develops practical solutions that improve mission outcomes. * Applies advanced conceptual, practical, and technical knowledge within data science and related disciplines and transfers that knowledge to others through mentoring, technical review, and collaboration. * Communicates complex ideas clearly, explains technical tradeoffs and limitations, and influences stakeholders to achieve project and mission objectives.