Lead Data Scientist

The Mitre Corporation is seeking a Lead Data Scientist to support MITRE’s Systems Engineering Analytics efforts by applying advanced data science, machine learning, AI, and data engineering. The role focuses on developing and evaluating data-driven capabilities for mission problems, including technical leadership, sponsor advising, and transition of prototypes and AI models into operational use.

Role Overview

In this position, you will apply statistical foundations and hands-on engineering practices to prepare and analyze complex data, build and assess models, and deliver analytic capabilities that support mission outcomes. You will also advise government sponsors on technical approaches and implementation plans, contribute to data and analytics strategy, and serve as a trusted technical point of contact for assigned projects.

Key Responsibilities

• Apply strong foundations in statistical analysis, machine learning, AI, and software development to develop, evaluate, and deliver analytic solutions for mission problems.
• Support government sponsors in developing and evaluating technical approaches and implementation plans for adoption of data-driven capabilities across the national security community.
• Contribute to data and analytics strategies by assessing the suitability of data, models, and supporting architectures for mission applications and providing evidence-based decision support.
• Produce clear, actionable deliverables and communicate complex technical concepts and analytic findings to decision makers.
• Lead technical efforts, mentor colleagues, and collaborate with engineering teams to transition analytic approaches into operational capabilities.
• Translate complex mission needs into analytical questions, measurable objectives, and implementable technical approaches, including the use of statistical inference, experimental design, causal inference, or simulation to support defensible recommendations.
• Develop and evaluate statistical, machine learning, and deep learning models for areas such as prediction, classification, forecasting, anomaly detection, natural language processing, computer vision, graph analytics, or multimodal analysis.
• Prepare, integrate, and engineer large heterogeneous datasets, including entity resolution and schema harmonization, while addressing 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 models and multimodal models, using prompting, retrieval-augmented generation, fine-tuning, and transfer learning with connections to enterprise data and tools.
• Develop AI evaluation datasets and benchmarks to assess accuracy, grounding, hallucinations, robustness, and operational suitability, including 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.
• Build scalable batch and streaming data pipelines using distributed processing and workflow orchestration, with attention to 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 versioning of data, code, and models.
• Support transition of analytic prototypes and AI models into operational capabilities with engineering teams, including deployment readiness, integration, performance monitoring, drift detection, retraining, and rollback across the model lifecycle.
• Lead technical teams, analytical workstreams, or projects with moderate risk and resource requirements, working independently with guidance primarily in highly complex situations.
• Resolve complex analytical problems and deliver practical solutions that improve mission outcomes through mentoring, technical review, and collaboration.

Required Qualifications

• 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, or a PhD in a related field with 3 years.
• 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 building reproducible data preparation, analysis, and modeling workflows.
• Ability to quickly 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 to technical and non-technical audiences.
• Strong written and verbal communication skills.
• Experience collaborating with multidisciplinary teams 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 clearance. You must be a U.S. Citizen to be considered for a security clearance.
• Minimum of 4 days a week on-site.

Preferred Qualifications

• 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.

Technologies and Methods

• Programming and data: Python, R, SQL, NoSQL
• Modeling areas: natural language processing, computer vision, graph analytics, multimodal analysis
• Foundation model techniques: large language models, retrieval-augmented generation, fine-tuning, transfer learning
• Data platforms: vector databases, lakehouse platforms, warehouses, lakes
• Databases and architecture: relational databases, graph databases, semantic models, metadata, lineage, governance, access controls
• Engineering and operations: distributed processing, workflow orchestration, experiment tracking, model registries, feature engineering, feature pipelines
• Validation and optimization: cross-validation, temporal and out-of-distribution testing, hyperparameter optimization
• Lifecycle management: drift detection, retraining, rollback
• Research methods: causal inference, simulation

Location and Clearance

• Location: McLean, VA (onsite)
• On-site requirement: Minimum of 4 days a week
• Minimum clearance required: Secret
• Clearance to obtain within one year of hire: Top Secret/SCI

Compensation

Annual salary range: $155,200 - $194,000 - $232,800