Top Data Science Jobs in Baltimore, MD

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Lead the development of statistical and machine learning models for time-series and geospatial data. Build analytic solutions, datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and reports. Evaluate algorithm performance, optimize classifiers, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders to deliver reliable mission-focused software and data capabilities.

Develops statistical and machine learning models for kinematic behavior detection using live, historical, time-series, and geospatial data. Responsibilities include data integration, ETL, feature engineering, forecasting, anomaly detection, algorithm evaluation, simulation, visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to mission-focused software applications, APIs, data workflows, code reviews, troubleshooting, and agile development in a remote-first environment.

Lead statistical and machine learning model development for time-series and geospatial data supporting the FALCON mission. Responsibilities include feature engineering, forecasting, anomaly detection, ETL, dataset creation, algorithm evaluation, simulation, data visualization, reporting, documentation, and stakeholder collaboration. The role also contributes to software applications, APIs, data pipelines, code reviews, troubleshooting, and iterative agile development.

Perform advanced data exploration, engineering, and machine learning within Palantir Foundry. Develop Foundry applications, pipelines, and workflows; implement DevSecOps and CI/CD; engage stakeholders to translate requirements into technical solutions for Department of War clients. Work in agile teams supporting scalable analytics platforms; a Secret clearance is required.

Develop graph data models, optimize Neo4j and Cypher queries, build Python data parsers, validate mission data, and support near-real-time graph analysis. Translate analyst and customer requirements into graph-based solutions, collaborate with software developers, document schemas and processes, and address operational challenges through data modeling and graph technologies.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Develops Python-based data processing workflows, rapid prototypes, and statistical models for sensor and SIGINT collection performance. Integrates structured and unstructured data through APIs and mission repositories, analyzes metrics and trends, and presents findings through visualizations, dashboards, and reports. Collaborates with mission stakeholders to refine requirements and deliver actionable analytics supporting targeting and intelligence missions.

Analyze and visualize complex RF, ELINT, and Technical SIGINT datasets supporting Advanced Weapons and Counter Proliferation missions. Develop data workflows, actionable intelligence products, visualizations, and analytical methods while validating data quality and documenting findings. Collaborate with engineers, analysts, and mission partners on RF technology identification, mission planning, and technical intelligence analysis.

Analyze complex structured and unstructured datasets, develop statistical and machine learning models, build data pipelines, and create visualizations supporting mission and business decisions. The role requires strong Python-based analytics, data wrangling, database management, data quality practices, and stakeholder collaboration. Candidates must have an active TS/SCI with polygraph, a bachelor’s degree, and at least five years of relevant experience.

Develop and evaluate machine learning and statistical models supporting intelligence analysis. Curate, transform, and integrate data; build classifiers and anomaly detection systems; deploy and refine models in production; monitor model performance and data accuracy; collaborate with analysts and software developers; document processes and datasets; and communicate findings to stakeholders.

Support Computer Network Operations by applying AI and machine learning to network exploitation tools, data, and workflows. Evaluate large language models, analyze mission data using statistical methods, develop Python-based analytic and ML workflows, normalize and visualize data, and collaborate with operators, developers, and engineers to deliver mission-focused technical solutions.

Own product analytics for a healthcare platform by translating business questions into research plans, uncovering insights, modeling feature impacts, defining success criteria, and leading A/B testing. Partner with Product, Data Engineering, Finance, Clinical, and Operations teams to influence roadmap decisions, improve provider experience and quality, and optimize marketplace performance.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Partner with clients to design healthcare data science solutions, curate multimodal clinical datasets, and conduct statistical, predictive, causal inference, real-world evidence, and AI/ML analyses. Responsibilities include querying EMR and other health data sources, creating statistical analysis plans, managing SQL and analytical programming, ensuring HIPAA-compliant data quality, documenting findings, and presenting results to technical, clinical, and executive audiences.

Develop foundation models for longitudinal wearable and physiological data, leading research from hypothesis development through modeling, experimentation, evaluation, prototyping, and product deployment. Build datasets and ML infrastructure, advance representation learning, forecasting, personalization, multimodal and generative modeling, and establish robust evaluation methods. Collaborate with scientists, clinicians, engineers, and product teams while translating research into health experiences.

Lead development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Build analytic solutions, training datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and performance metrics. Integrate multi-source sensor data, enhance algorithms, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders in an agile environment.

Analyze complex program, operational, schedule, requirements, risk, and workstream data to develop metrics, models, dashboards, and decision-support insights. Assess data quality, collaborate with architects and engineers on analytics readiness, support AI-enabled workflows and human-in-the-loop validation, and communicate findings to technical and mission stakeholders. The role supports FAA modernization and Digital Command Center analytics.

Own the company-wide revenue retention and add-on monetization analytics strategy. Build causal inference and retention models covering churn, MRR, GRR, NRR, attach, and usage. Partner with Finance, RevOps, Customer Success, CPaaS, AI, and product leaders to establish governed metrics, identify opportunities, guide experimentation, and influence product, pricing, and lifecycle decisions. Set analytical standards, develop reusable methods, and provide technical direction across teams.

Develop machine learning and advanced analytics models for sparse, noisy, and adversarial intelligence datasets. Translate analytic questions into technical requirements, assess data quality, recommend technical approaches, communicate findings to technical and non-technical stakeholders, and support automated analytics workflows for national security missions.

Conduct data analytics, engineering, mining, statistical analysis, and predictive modeling for USCYBERCOM J2 intelligence operations. Build AI and machine-learning systems, manage disparate data sources, create Power BI and Tableau visualizations, and produce analytic intelligence products. Collaborate with data scientists, analysts, and collection managers while supporting Multi-INT analytics and maintaining operating instructions.

Develop machine learning, statistical, data-mining, graph-based, and heuristic algorithms for national security datasets. Build, optimize, and validate AI/ML and large language models; create feature vectors, experiments, simulations, visualizations, and automated analytics; and integrate prototypes into production analyst workflows. Collaborate with subject matter experts, evaluate models using statistical metrics, and guide analytic development efforts. The role requires an active TS/SCI clearance with polygraph.

Develop machine learning, statistical, data mining, graph-based, and heuristic algorithms for national security applications. Build and optimize AI/ML and large language models, including information retrieval workflows. Analyze varied datasets, create feature vectors, design experiments, evaluate models using standard metrics, produce visualizations, and translate subject matter expertise into production analytics. Guide analytic development efforts and integrate prototype algorithms into analyst workflows.

Develops machine learning, statistical, data mining, graph-based, and large language models for national security applications. Analyzes datasets, creates feature vectors, prototypes and validates algorithms, builds experiments, generates visualizations, and implements analytics in production frameworks. The role collaborates with subject matter experts, optimizes GPU-based model training, supports LLM and RAG solutions, and guides analytic development efforts. An active TS/SCI clearance with polygraph is required.

Develop and operationalize machine learning, statistical, graph-based, and AI analytics for national security missions. Responsibilities include applied research, experimentation, data visualization, feature engineering, model development and validation, LLM and NLP optimization, GPU-accelerated computing, automation, production integration, and customer collaboration. The role also guides analytic efforts and translates complex data into actionable insights.