Top Senior Data Science Jobs in Baltimore, MD

Provide advanced discovery support using machine learning, statistical analysis, scripting, automation, and TechSIGINT tools. Develop analytical prototypes, create data visualizations, automate workflows, and collaborate with analysts and mission stakeholders to identify patterns and drive investigations.

Support CNO mission areas by applying AI/ML and Python to develop automation and analytical workflows, perform collection processing and data analysis, create and maintain Jupyter Notebooks for prototyping, and collaborate with technical and operational stakeholders while rapidly adapting to changing mission priorities.

Design and automate scalable ETL/ELT and reporting workflows, perform data cleansing/validation/transformation, build reusable Python automation and SQL data-processing pipelines, support dashboard automation, improve data quality and reliability, and document data flows and processes.

Develop NLP and NER capabilities for LLM-based, Agentic AI solutions (e.g., LangGraph). Train and validate supervised and unsupervised models, build preprocessing and data-retrieval pipelines, perform automated knowledge extraction from multilingual unstructured text, integrate customer data sources, and ensure data quality, governance, and policy compliance to meet organizational and end-user needs.

Provide advanced discovery and analytical support using machine learning, statistical analysis, scripting, automation, and TechSIGINT tools. Develop analytical prototypes, create data visualizations, document methods and collaborate with analysts and engineers to support mission-focused discovery efforts and adopt new analytical technologies.

Develop and improve NLP solutions for spoken and written language data, including automated tokenization and linguistic annotation, analyze speech/text datasets, evaluate model performance against human annotations, and document methods and recommended improvements.

Perform data and computer science analysis to support customer operations using advanced machine learning, statistical methods, and programming (Python, Java, C/C++). Curate and process large, messy national agency datasets, build models and inferences, translate mission requirements into technical solutions, and communicate results across teams. TS/SCI with polygraph clearance required.

Senior individual contributor who drives product analytics, experimentation, and applied modeling. Owns growth and performance analyses, designs experiments with robust statistical evaluation, builds repeatable reporting, and partners with Product, Engineering, Operations, Clinical, and Finance to translate data into actionable product and operational decisions.

Build and scale production Generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with vector DBs, integrate and fine-tune LLMs, deploy FastAPI async services in Docker, and optimize GenAI performance, cost, and monitoring.

Lead experimentation and decision science initiatives from problem definition through recommendations. Design and analyze A/B tests, quasi-experiments, matched-market tests, and causal inference studies. Apply advanced statistics, regression, predictive modeling, machine learning, and big-data analysis using Python, R, SQL, and PySpark. Partner with stakeholders, communicate actionable findings, build reproducible workflows, and mentor analysts and junior data scientists.

Build, validate, deploy, monitor, and maintain predictive models and analytical reports. Translate findings for technical and non-technical stakeholders, promote data literacy, deliver training, collaborate on data infrastructure, apply advanced statistical and machine learning methods, and support analytical requests across mental health and crisis care programs.

Senior individual contributor owning complex end-to-end ML problems for supply chain and consumer applications. Build and deploy predictive models (demand forecasting, delivery ETA, recommendations), embed with engineering teams, define MLOps standards, and advise leadership on data science strategy and investments.

As the Staff Data Scientist, you will lead the data science function, driving strategy, mentoring peers, and shaping product direction through advanced analytics and experimentation.

Lead company-level measurement and causal strategy, define MAU×ARPU metrics and semantic mart, set scientific standards for experimentation and analysis, own strategic models (MMM, elasticity, forecasting), partner with engineering to productionize analytics, and raise org-wide scientific maturity through governance, templates, and upskilling.

Own and evolve revenue calibration models tying CX interactions to revenue, design causal inference and experimentation frameworks, build LLM-powered classification/NLP pipelines, productionize models with Analytics Engineers, define segmentation and behavioral signals, and ensure statistical rigor for executive reporting and regulatory defensibility.

Lead analytic approaches to extract meaning from large, messy NSA/CSS datasets. Design and implement ML and statistical models, manage/curate data, translate mission needs into technical requirements, and communicate results to non-technical stakeholders.

The Staff Data Scientist will lead the development and deployment of ML and GenAI systems to detect billing errors and fraud in healthcare payments, collaborating with cross-functional teams.

The Data Scientist will develop tools for enhancing signature management capabilities, integrate diverse data holdings, and leverage AI/ML solutions for operational support.

Develop a tool to enhance signature management capabilities and integrate diverse data holdings, leveraging AI/ML solutions to meet operational needs.

Build and productionize ML models and data infrastructure to analyze millions of chaos engineering experiments, detect and explain failures, recommend automated remediation, and integrate AI-driven failure analysis into Gremlin's product using techniques like causal inference, graph ML, time-series modeling, and reinforcement learning.

Design and deploy ML systems for enterprise search, recommendations, retrieval, and AI agents. Build scalable data/ML pipelines, evaluation frameworks, and metrics. Apply statistics, experimentation, and causal inference to guide product decisions. Collaborate cross-functionally, prototype LLM-based capabilities (embeddings, RAG), and mentor junior team members.

Lead design and deployment of ML systems for enterprise search, retrieval, recommendations, and AI agents. Build scalable data/ML pipelines, evaluation frameworks, and KPIs. Apply statistics, causal inference, experimentation, and analysis to guide product strategy. Prototype and productionize LLM-based features, mentor junior staff, and collaborate cross-functionally.

Lead development and deployment of machine learning, statistical, and graph-based algorithms on large datasets for Intelligence Community clients. Prototype and evaluate models, design experiments, generate visualizations and reports, translate results into actionable recommendations, automate analytics into production, assess client readiness, and mentor/lead a team of data scientists while staying current with emerging ML techniques.

Build and deploy end-to-end ML models for renter acquisition, supply optimization, ranking, personalization, and marketplace optimization. Lead technical projects from problem framing through experimentation, launch, monitoring, and iteration. Apply statistical methods, causal inference, and rigorous A/B testing. Partner cross-functionally, mentor other data scientists, and raise modeling and experimentation standards.

Design and implement statistical, graph-based, and machine learning algorithms to analyze disparate cybersecurity datasets. Clean and structure data, produce visualizations for technical and non-technical audiences, translate analyst processes into quantitative solutions, validate analytics, and collaborate with engineers, cloud developers, and subject-matter experts to scale prototypes into production to support threat intelligence and defense missions.