Top Data Science Jobs in Baltimore, MD

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.

Analyze large-scale datasets; design, develop, evaluate, and deploy AI/ML and predictive/statistical models; build data models, visualizations, and automated analytical workflows; process data using Spark and cloud platforms; apply TechSIGINT tools and provide discovery-focused analytical prototyping.

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.

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.

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.

Perform advanced data science and machine learning on large NSA/CSS datasets. Design, implement, and validate analytic models, manage and curate data, extract actionable insights, and communicate technical conclusions to non-technical stakeholders. Translate mission needs into technical requirements while staying aware of collection and processing constraints.

Develop and apply statistical and machine-learning methods to large, messy NSA/CSS datasets. Design, implement, and evaluate analytic models; perform data cleaning, management, visualization, and inference; translate mission needs into technical requirements and communicate findings to non-technical stakeholders.

Develop and maintain data ontologies and metrics, analyze large mission datasets with Python/Jupyter/Pandas, automate workflows, build visualizations and dashboards, interact with APIs, identify data/technical gaps, and present findings to government stakeholders.

Perform statistical and computational analysis on large, messy datasets to produce defensible conclusions. Build analytic models, perform statistical inference and prediction, translate mission needs into technical requirements, and communicate results to non-technical stakeholders. Maintain awareness of collection/processing limitations and recommend technical solutions.

Build and productionize ML systems for personalization, search, and recommendations. Apply statistical and ML models using Python and SQL, collaborate with stakeholders, and measure business impact across cloud environments.

The Data Scientist will develop data labeling strategies, optimize datasets for LLMs, analyze data quality, and collaborate with engineering on data processing pipelines.

The Data Scientist will work on data analysis, visualization, and reporting, utilizing various tools to assist stakeholders' needs.

Build lightweight, Slack- and Sheets-integrated AI agents to pull and analyze data from ad and web APIs, surface anomalies and trends, and produce alerts, reports, and LLM-generated content briefs and recommendations. Deliver scoped, fast iterations on multi-step pipelines including classifiers, scorers, and recommendation layers.

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.

Develop and sustain analytics tools using Python and customer toolset. Apply mathematical, statistical, and computational methods to large, messy national agency datasets, build and validate models, translate mission needs into technical requirements, and communicate complex results to non-technical audiences.

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.

Build and operationalize machine learning and AI solutions for hospitality pricing, forecasting, and revenue management. Analyze large datasets, write Python and SQL to prepare and validate data, and partner with product and engineering to turn analyses into product capabilities. Apply modern AI techniques, including LLMs and AI coding tools, and communicate insights to stakeholders.

Design, develop and deploy machine learning and time-series models for water, wastewater and infrastructure systems; work with operational telemetry (SCADA/AMI/IoT), geospatial and graph analytics; integrate models into production pipelines with MLOps and monitoring; communicate results to stakeholders; perform Physical AI research and mentor junior data scientists.

Design measurement frameworks and advanced causal inference solutions for marketplace allocation and recommendation systems. Partner with Product, Engineering, and business stakeholders to translate needs into scalable data science tools, run experiments, quantify impact, and drive implementation for Netflix discovery surfaces.

Support IRS projects by developing, validating, and deploying predictive and statistical models on large tax and financial datasets. Perform data cleaning, feature engineering, anomaly detection, and risk scoring using Python/R, SQL, and Databricks. Communicate results to stakeholders and collaborate with cross-functional teams to inform audit prioritization, fraud detection, and compliance efforts.

Design, develop, validate, and deploy statistical and machine learning models to detect fraud, anomalies, and noncompliance in large tax and financial datasets. Clean and feature-engineer data, run robustness and sensitivity tests, collaborate with stakeholders, and present technical and non-technical findings. Support end-to-end data science lifecycle and productionization in a government environment.

Lead and support full-lifecycle data science projects (forecasting, NLP, classification/regression, optimization). Design production-ready solutions within client environments, consult with stakeholders to define ambiguous problems, communicate technical findings, and develop/teach bespoke analytics training.

Support IRS tax compliance and fraud detection by developing, validating, and deploying predictive and statistical models. Clean and analyze large datasets, apply supervised and unsupervised learning (including NLP and graph analytics), collaborate with clients and teams, and deliver technical briefings and defensible, explainable model results.