Top Data Science Jobs in Baltimore, MD

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.

Design and implement reinforcement learning models and sequential behavioral models to optimize collections and treatment strategies. Build scalable ML pipelines (Databricks), run simulations and offline policy evaluation, and collaborate with stakeholders to deploy adaptive decisioning systems under uncertainty.

Lead Oracle-focused healthcare data quality and analytics: validate transactional data, build standardized KPIs and dashboards, develop predictive inventory and revenue cycle models, and collaborate with clinical and finance teams to ensure compliance and optimize operations.

Build and deploy predictive and time-series models using HR and clinical data (Oracle HCM, GCP) to forecast staffing, identify flight-risk employees, analyze pay/burnout relationships, and create interactive dashboards and analyses to inform HR strategy and operational workforce decisions.

Design, build, and deploy machine learning and generative AI models (including LLMs and text-to-image). Analyze large datasets, run A/B and statistical tests, collaborate with data engineering and engineering teams, and create dashboards/visualizations to communicate insights. Optimize data pipelines and model performance to support data-driven decisions.

Turn multi-model signals into measurement, forecasts, and optimizations. Clean and label large datasets, extract structure with LLMs/NLP, define product-grade metrics, run experiments, build predictive models, create evals and monitors, and translate insights into product and business decisions.

Perform data cleansing, ETL, and preprocessing; analyze Retail CPG sales, inventory, and customer data; build predictive models for forecasting and segmentation; research and deploy Agentic frameworks and intelligent agents; produce visualizations and communicate insights to cross-functional stakeholders to optimize supply chain, marketing, and customer experience.

Build and scale production-ready generative AI applications: design multi-agent workflows, implement advanced RAG with vector DBs, integrate/swap LLMs, fine-tune models, and deploy scalable FastAPI/Docker services with monitoring and optimization for latency, cost, and reliability.

Design, build, and deploy production-ready generative AI applications: architect multi-agent workflows, implement advanced RAG with vector DBs, integrate/swap LLMs, fine-tune models, and deliver scalable FastAPI-based services with monitoring, CI/CD, and containerized deployment.

Design, build, and scale production-ready generative AI systems: multi-agent workflows, advanced RAG pipelines, LLM integration and fine-tuning, scalable FastAPI backends, Dockerized deployments, monitoring, and GenAI optimization for latency, cost, and reliability.

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.

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.

Perform data science on government datasets: extract VPN info, apply DX/XKS and CASN knowledge, build models, curate and analyze large datasets, translate mission needs into technical requirements, and communicate findings to non-technical stakeholders.

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.

The Data Scientist Level 4 develops strategies to extract insights from large datasets, utilizing advanced analytics and machine learning techniques. Responsibilities include statistical analysis, modeling, and effectively communicating findings to non-technical audiences.

The Senior Data Scientist will build models and analyses, design experiments, integrate datasets, and leverage AI for improved workflows and insights in data science.

Perform advanced data exploration, engineering, and machine learning within Palantir Foundry. Develop Foundry applications, pipelines, and workflows; implement DevSecOps and CI/CD; engage stakeholders to translate requirements into technical solutions for Department of War clients. Work in agile teams supporting scalable analytics platforms; a Secret clearance is required.

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

The Data Scientist II will drive insights, develop models, and analyze product experiments to inform business decisions and improve member experiences in healthcare.

Designs, analyzes, and interprets clinical trial, RWE, and observational data for a medical device. Develops statistical plans, predictive models, and ensures data quality, regulatory compliance, and traceability. Collaborates with clinical, regulatory, and data teams, supports submissions and publications, mentors interns, and represents the company externally.

Build and productionize machine learning models for healthcare products, define and track KPIs, collaborate with product and customers to scope analytics projects, communicate results to technical and non-technical stakeholders, and improve analytics practices across the team.

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.

As a Senior Data Scientist, you will analyze complex datasets, develop algorithms, and apply machine learning to solve critical business problems across various sectors while collaborating with clients to provide actionable insights.

The Data Scientist II will design, build, and deploy machine learning models and data products, translating business problems into data-driven solutions, while collaborating with various stakeholders and ensuring reliable, production-ready outcomes.