Top Data Science Jobs in Baltimore, MD

Develop automated security evaluation and authorization capabilities using Python, statistical analysis, machine learning, anomaly detection, and ETL pipelines. Build reliable dataflows integrating network, security, and compliance data to support continuous monitoring, RMF automation, reporting, and security decision-making. The role requires extensive analytics and programming experience and a TS/SCI clearance with polygraph.

Analyze, manipulate, and visualize complex mission data to support AWCP activities; develop analytical methods, workflows, and visualizations; research and correlate multi-source information; document findings and collaborate with technical and mission teams.

Apply statistical and computational techniques to large complex datasets to develop, evaluate, and deploy analytical models and reproducible workflows. Clean, transform, and visualize data, communicate findings to technical and non-technical stakeholders, and translate mission requirements into operational data solutions.

Design, implement, and deploy AI/ML solutions (including LLMs and autonomous agents) to analyze large, complex CNO datasets, develop scalable analytical methods, derive mission insights, and collaborate with technical and mission teams while documenting methods and results.

Support and manage Big Data platforms, analyze large mission datasets, develop and refine prototype algorithms, build automated data-processing workflows, create visualizations, and document methodologies while collaborating with technical and mission teams to deliver analytic solutions that support operational objectives.

Work with large-scale datasets and distributed environments to build scalable data-processing workflows and automated pipelines using Python and Spark. Apply network and protocol analysis to communications data, transform and integrate multiple data sources, document methods, and collaborate with technical and mission teams to deliver analytic solutions supporting mission requirements.

Design and implement data-driven automation for security authorization and continuous RMF monitoring. Build ETL pipelines, process and validate security data, detect anomalies, develop predictive models and dashboards, and support data engineering for scalable security assessments.

Lead the strategic direction and evolution of Netflix’s company-wide Experimentation Platform. Establish rigorous causal inference and experimentation standards, automate trust and validation practices, consolidate fragmented systems, shape platform product roadmaps, and collaborate with data science and engineering leaders. Translate experimentation needs into scalable tools, influence adoption across teams, mentor colleagues, and ensure reliable experiment allocation, measurement, analysis, and reporting.

Senior Data Scientist responsible for analyzing intelligence knowledge-management and production data, building executive dashboards, and developing automated ETL/ELT pipelines. The role forecasts analytical production and workforce resourcing, defines KPIs with leadership, translates statistical models into executive decision aids, and ensures data integrity, auditability, and governance. Work includes Python or R, advanced SQL, BI platforms, metadata management, and API integrations with Jira, Git, and ServiceNow. The position requires TS/SCI clearance and an initial 3–6 month embed at DIA HQ.

Lead and deliver end-to-end data science projects: design, build, deploy, and monitor ML/statistical models for credit risk, pricing, collections, and fraud. Partner with cross-functional teams to integrate models into production, set model development standards, mentor engineers, and maintain reliable ML pipelines and monitoring systems.

Analyze and characterize conversational audio data, identify underrepresented areas, and build active-learning and human-in-the-loop workflows. Develop representative benchmarks, automate model adaptation, and create reusable data analysis and scoring systems. Partner with research, engineering, and data teams to run experiments, improve models, organize data, and translate complex findings into measurable product improvements.

Design, develop, and deploy supervised machine learning models for credit decisioning. Collaborate with business and technical stakeholders to create data sources, improve modeling methodology, operationalize models, and manage model evaluation and risk for production systems.

Lead G2’s experimentation practice across products and digital marketplaces. Design experimentation standards, causal inference methods, metrics, guardrails, and analysis approaches; partner with product and engineering to implement experimentation platforms and instrumentation. Build models, design auctions, ship production systems, evaluate modern statistical methods, and address marketplace interference and low-traffic constraints. Mentor data scientists and evangelize rigorous data science practices across the organization.

Leads data science initiatives by analyzing complex datasets, developing and deploying machine learning and generative AI solutions, and applying RAG, fine-tuning, and agentic workflows. Improves healthcare outcomes, Star ratings, care gaps, payment integrity, and member experiences. Collaborates with technical and business teams, communicates analytical findings, documents methodologies, manages projects, and mentors data scientists.

Support national security operations by applying machine learning, statistical analysis, and scalable data processing to large datasets. Build automated pipelines, curate and transform data, develop and refine analytical algorithms and models, create visualizations and dashboards, and translate mission requirements into reproducible analytics to inform stakeholders.

Apply advanced data science and ML techniques to national security problems: curate, transform, analyze large SIGINT/RF/PROFORMA datasets; build reproducible analytics, models, and visualizations; automate workflows and optimize data pipelines; communicate results to technical and non-technical stakeholders.

Apply advanced data science, machine learning, and statistical techniques to large-scale cyber and network datasets. Build data pipelines, curate and normalize data, develop and refine analytical models (including NLP/LLMs), create visualizations and automated workflows, and translate mission requirements into actionable analytics to support national security operations.

Leads graph analytics, identity resolution, forecasting, and predictive modeling initiatives for operational, commercial, and product decisions. Designs scalable graph data models, production-ready machine learning solutions, and time-series forecasts. Establishes validation, monitoring, governance, and experimentation practices while partnering with product, engineering, and platform teams. Translates complex analytical findings into business recommendations and provides technical leadership across graph science, forecasting, optimization, and decision-support capabilities.

The Senior Data Scientist will analyze large-scale customer data, investigate behavioral questions, design and automate studies, and translate findings into actionable recommendations. The role includes maintaining analytics frameworks, documenting methodologies, writing white papers, and communicating insights across the company. Candidates should be independent, comfortable with ambiguity, and experienced with SQL and Redshift. Python or R, machine learning, Spark, A/B testing, and cross-functional experience are preferred.

Perform data extraction (including VPN data), integrate into databases, and analyze datasets using statistical and computational methods. Apply knowledge of DX, XKS, and CASNs to guide data selection, build analytic models, and produce actionable insights. Translate mission needs into technical requirements and communicate results to non-technical stakeholders while accounting for government data constraints.

Partner with product engineering to design and analyze experiments, apply causal reasoning and statistical modeling to marketplace data, and drive data-informed product decisions across the engineering org.

Lead the data science strategy for deployed humanoid robots: build predictive maintenance and reliability models, drive fleet performance and RaaS economics analysis, develop anomaly detection and RCA tooling, link manufacturing test data to field outcomes, and set instrumentation, experimentation, and data-quality standards across hardware, software, manufacturing, and operations.

Lead evaluation and data strategy for advanced AI, vision, and perception systems. Define metrics, build evaluation frameworks, audit artifacts, prioritize fixes, and translate model behavior into product decisions and data-improvement loops to ensure production readiness and downstream usefulness.

Provide analytics and data-driven insights for CoverMyMeds clients, prioritize work that drives commercial outcomes, build relationships with account teams, apply statistical and predictive methods, monitor portfolio health, deliver client-facing visuals and recommendations, support complex requests and escalations, and inform strategic decisions across product, analytics, and operations.

Leads technical support for VA healthcare AI citizen developers, developing and validating supervised, unsupervised, and reinforcement learning models. Builds synthetic healthcare data cohorts, designs secure cloud-based data science workflows, collaborates with federal stakeholders, interprets analytical results, mentors developers, and ensures model quality, documentation, privacy, governance, and regulatory compliance.