Top Senior Data Science Jobs in Baltimore, MD

Leads analytical and scientific strategy for Fetch’s Play and Rewarded Apps businesses. Develops predictive and machine learning models, experimentation frameworks, causal analyses, feature pipelines, and measurement systems using Python and SQL. Partners with Product, Marketing, Engineering, and business leaders to improve monetization, engagement, retention, personalization, and marketplace outcomes. Translates complex findings into strategic recommendations, mentors data scientists and analysts, and establishes analytical best practices.

The Lead Data Scientist will enhance analytics through predictive modeling and data engineering, focusing on quality, feature generation, and continuous learning.

Develop and migrate reporting assets from SAP BusinessObjects to QlikView and Qlik Sense. Analyze and rebuild Web Intelligence, Crystal Reports, and universes; debug legacy and new reports; optimize SQL, scripts, expressions, data models, and load processes; validate output accuracy; document migration logic; and collaborate with stakeholders on requirements and report specifications.

Lead development and productionization of telematics pricing and underwriting models. Responsibilities span statistical modeling, feature engineering, dataset and pipeline development, model deployment, monitoring, data quality validation, troubleshooting, regulatory support, and collaboration with pricing, actuarial, product, engineering, and compliance teams.

Develops scalable data pipelines, parsers, and database systems for enterprise AI applications. Collaborates with AI/ML teams to optimize data access, retrieval, quality, security, and infrastructure performance. Supports AI application deployment and maintenance while evaluating machine learning models and statistical techniques. The role requires expertise in Python, databases, Docker, Kubernetes, data engineering, defensive cyber operations, and machine learning, with potential supervision of others.

Develop and optimize scalable data pipelines, parsers, and database systems supporting enterprise AI applications. Ensure data quality, security, and efficient retrieval across graph, SQL, NoSQL, and vector databases. Collaborate with AI/ML teams, support AI application deployment, and optimize infrastructure for performance, cost, and scalability. The role requires advanced data engineering expertise, cloud familiarity, and an active TS/SCI clearance with polygraph.

Develop and maintain advanced data processing, entity resolution, and data cleansing pipelines using Python and SQL. Responsibilities include NLP, information extraction, data validation, query optimization, testing, deployment, monitoring, code reviews, and maintaining data integrity, privacy, security, and reproducibility. The role requires expertise in record linkage, named entity recognition, text processing, matching strategies, and machine learning libraries, with on-site work at SSA headquarters in Woodlawn, Maryland.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Design, build, evaluate, and productionize agentic AI, LLM, RAG, and predictive ML systems for scientific information products. Develop training and inference pipelines, fine-tune models, establish trustworthy-AI evaluation and safeguards, and operate AI capabilities at scale. Set engineering standards, lead technical initiatives, mentor scientists and engineers, influence product and executive decisions, and communicate results through presentations, publications, and client engagements.

Tune AML and fraud detection models using customers’ live transaction data. Investigate missed cases, alert quality, pattern drift, and false positives; validate changes through backtesting and sample review; document regulatory rationale; and present model performance and business value to compliance, risk, and data science stakeholders. Partner with customer teams on account strategy and relay customer-specific or systemic findings to product and model functions.

Own mobile marketing analytics frameworks, dashboards, cohort and campaign analysis, profitability modeling, audience strategy, and incrementality experiments. Analyze attribution, retention, LTV, CAC, ROAS, payback, creative performance, and marketplace dynamics. Partner with marketing, finance, and leadership to guide spend allocation and growth decisions while navigating mobile privacy and attribution challenges.

Own sales forecasting and revenue analytics by developing statistical and machine learning models across products, regions, and time horizons. Lead the full machine learning lifecycle from feature engineering through deployment, monitoring, and explainability. Translate forecasts into business recommendations, deliver production data products, mentor junior data scientists, and provide technical leadership across analytics and sales teams.

As a Data Scientist Level 3, you will apply data science expertise to national security challenges, develop analytical tools, and communicate insights effectively to diverse audiences.

As a Data Scientist, Level 3, you'll leverage Python to automate workflows, perform data manipulation, and create visualizations, applying advanced analytics to complex datasets and communicating insights to varied audiences.

Leads end-to-end applied data science and AI workstreams for predictive underwriting and pricing, including problem framing, feature engineering, model development, validation, deployment, monitoring, and business-impact measurement. Builds explainable, governed, and reusable production models using healthcare and insurance data. Partners with underwriting, business, product, engineering, and legal teams; mentors others; and advances responsible AI, MLOps, model governance, and shared data science practices.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

As a Senior Data Scientist, you'll develop fraud detection models, perform detailed analyses, and work closely with teams to drive product innovation and business outcomes.

Develop AI tools, machine learning systems, predictive analytics, data visualizations, and large-scale Multi-INT data fusion capabilities for USCYBERCOM J2 intelligence operations. Analyze and merge disparate data using R, Python, and SQL; create Power BI and Tableau visualizations; produce analytic reports; and collaborate with intelligence stakeholders to prioritize capability gaps.

The Senior Data Scientist will develop data pipelines for AI applications, optimize data structures, manage databases, and ensure data quality and security.

Design, implement, and deploy AI/ML solutions and autonomous agents using LLMs, reinforcement learning, and agentic AI. Optimize and scale neural models, apply prompt engineering and RAG, document results, follow software development best practices, and collaborate with mission teams to support operational objectives.

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.

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.

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.