Top Data Science Jobs in Baltimore, MD

Lead performance marketing data science initiatives focused on lead segmentation, bidding optimization, targeting, and marketing spend allocation. Develop and deploy production-ready machine learning systems, establish research and ML operations roadmaps, build monitoring frameworks, and identify high-impact opportunities. Partner with engineering, marketing, vendors, and executives while mentoring data scientists and machine learning engineers and promoting rigorous, maintainable, and reproducible practices.

Lead data science for Mozilla’s advertising business by identifying high-impact opportunities, owning complex analyses, developing measurement and experimentation frameworks, analyzing revenue and performance, and translating findings into recommendations for technical partners and executives. Partner across Product, Engineering, Sales, Finance, and leadership to support business decisions in a fast-moving environment while balancing analytical rigor, pragmatism, and user privacy.

Lead data science partnership for Mozilla’s advertising business, identifying high-impact opportunities and owning complex analytics end to end. Develop measurement frameworks, experimentation strategies, instrumentation, and reporting for advertising products. Analyze revenue and performance, evaluate trade-offs, and communicate clear recommendations and uncertainty to technical teams and senior executives.

Principal Data Scientist supporting a U.S. Government customer by designing and implementing machine learning, data mining, statistical analysis, AI, and advanced analytics solutions. The role requires analyzing complex datasets, developing predictive and prescriptive models, using Python or R and database technologies, and providing technical expertise for mission-critical software and systems. It is based in Annapolis Junction with limited partial telework and requires an active or reinstatable TS/SCI with Polygraph clearance.

Develops retention, churn, and customer lifetime value models using statistical analysis and machine learning. Responsibilities include extracting and preparing structured and unstructured data, performing ad hoc analyses, creating dashboards and reports, automating analytical procedures, monitoring implemented models, and presenting findings to cross-functional business, IT, and management teams.

As a Staff Data Scientist at SentiLink, you will develop fraud detection models, conduct analyses, collaborate across teams, and drive the development of financial risk products.

Staff Data Scientist responsible for owning ambiguous, high-impact product and business problems; designing experiments; analyzing product data; developing analytical tools and frameworks; partnering with product, engineering, design, and leadership; and mentoring data scientists. The role requires advanced statistical expertise, strong product judgment, production programming experience, and the ability to translate complex analyses into strategic recommendations.

Conduct workforce analytics and applied organizational research on attrition, mobility, engagement, performance, and planning. Analyze and validate HR data using SQL, R, or Python; apply statistical testing and regression; translate business questions into actionable insights; and communicate findings through visualizations, reports, and presentations. Partner with HR stakeholders while maintaining the accuracy, documentation, confidentiality, and integrity of sensitive workforce data.

Develop and deploy machine learning algorithms that improve real-time device identification and fraud detection. Own data science projects from experimentation through production, including supervised, semi-supervised, and unsupervised learning, real-time inference, training automation, exploratory analysis, and model-to-service integration. Contribute to engineering best practices, collaborate within a global remote team, and participate in a shared on-call rotation.

Design, develop, and deploy ML, NLP, and generative AI solutions to improve scientific discovery. Build retrieval, recommendation, ranking, and RAG systems; fine-tune and evaluate LLMs; create evaluation frameworks; build scalable data pipelines; collaborate with cross-functional teams and produce production-quality Python code to deliver reliable, impactful AI systems for research use.

Design, develop, and deploy ML, NLP, and generative-AI solutions (including LLMs and RAG) to improve search, retrieval, recommendation, and question-answering across scientific publications and datasets. Build scalable pipelines, evaluation frameworks, and production-quality Python code while collaborating with engineering, product, UX, and domain experts to measure and improve model reliability, relevance, and user impact.

Develop analytical and AI-enabled capabilities for a federal data modernization effort. Translate stakeholder questions into statistical analyses, predictive models, forecasts, and scenario analyses; build governed datasets using Databricks, BigQuery, Python, SQL, and PySpark; validate and document models; maintain analytical products; and communicate visualizations, dashboards, findings, assumptions, and limitations to technical and non-technical audiences.

Assist senior data scientists with data collection, preprocessing, cleaning, and basic statistical analysis. Support model development, perform basic visualizations, and document processes and findings while learning and applying data science techniques.

Lead data science projects from ideation through enterprise impact, building scalable and well-tested solutions that improve healthcare outcomes. Apply probability, statistics, machine learning, SQL, and Python to develop data-driven products and insights. Partner with stakeholders, assess product performance, identify strategic improvements, mitigate risks, and mentor teammates while aligning initiatives with business objectives.

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.

Lead advanced research and predictive modeling on residential housing and energy performance data. Apply causal inference, anomaly detection, regression, and model validation to improve Pearl’s SCORE and energy models. Develop new performance metrics, evaluate climate-risk data, collaborate with external research partners, and communicate findings through white papers, publications, and clear stakeholder explanations.

Lead end-to-end data science for customer-embedded, production solutions: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, run experiments, deploy operational models at scale, coach cross-disciplinary teams, and drive strategy, ethics, and privacy discussions.

Develops data-driven solutions for government and strategic customers by acquiring and preparing datasets, evaluating and improving statistical models, building scalable operational models, researching industry trends, and collaborating with software engineers, technical program managers, designers, and customers on cloud-based AI projects.

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.

Leads data science engineering projects with government and strategic customers, overseeing data acquisition, preparation, modeling, evaluation, ethics, privacy, and scalable operational models. Coaches engineers, develops extensible code, researches industry trends, identifies strategic opportunities, and delivers customer-focused AI and cloud solutions. Requires advanced education or equivalent experience, extensive data science experience, active TS/SCI clearance with polygraph eligibility, U.S. citizenship, and Microsoft background screening.

Leads consumer personalization strategy and production development for recommendations, ranking, search, browse, carts, marketing, and agentic AI experiences. Builds recommender systems, learning-to-rank models, LLM agents, evaluation frameworks, and experimentation programs. Partners with Product and Engineering on deployment, monitoring, and model quality while mentoring senior data scientists, setting technical direction, shaping roadmaps, and communicating trade-offs to executives.

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.