Top Data Science Jobs in Baltimore, MD

Entefy seeks a Senior Data Scientist with a Master's or PhD in a relevant field, and 3+ years of experience. Responsibilities include delivering advanced analytics, leading teams, and extracting insights from datasets.

Design and implement analytic methods to extract meaning from large, messy NSA/CSS datasets. Build and evaluate models, manage and curate data, translate mission needs into technical requirements, and communicate technical conclusions to non-technical stakeholders while accounting for collection and processing constraints.

The Senior Data Scientist will design and implement machine learning solutions, conduct advanced statistical analyses, and manage data-related tasks, requiring a high level of expertise.

The position involves unlocking insights from data sets using IoT and AI, developing algorithms, and collaborating with clients to solve data-driven problems.

Design and apply statistical, mathematical, and computational methods to extract value from large, messy datasets. Build, evaluate, and communicate analytic and ML models, translate mission needs into technical requirements, and recommend solutions while explaining results to non-technical stakeholders.

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 apply machine learning and statistical analysis to support mission analysis, develop analytical tools, and visualize complex datasets for insights.

The Data Scientist will develop advanced NLP solutions, evaluate model performance, and collaborate on production system integrations for language data processing.

The Data Scientist will develop data-driven solutions, integrate APIs, manipulate data using Python, analyze large datasets, and collaborate with teams.

The Data Scientist will design and develop NLP and NER models, validate ML models, and ensure compliance for AI solutions, focusing on unstructured data processing.

Lead company-level measurement and causal strategy, define MAU×ARPU metrics and semantic mart, set scientific standards for experimentation and analysis, own strategic models (MMM, elasticity, forecasting), partner with engineering to productionize analytics, and raise org-wide scientific maturity through governance, templates, and upskilling.

Lead end-to-end supply chain planning science: own forecasting tournaments, inventory placement optimization, vendor performance systems, and raw-material signals. Define methodology, production models, and iteration loops; drive AI-native workflows (LLM-aided EDA, agentic feature engineering), partner with planning operators and engineering, and mentor junior scientists to raise methodological standards and deliver measurable business impact.

Lead end-to-end supply chain planning and forecasting modeling: demand forecasting across geographies, forecasting tournaments, inventory placement optimization, vendor performance modeling, and raw-material signal generation. Collaborate with leadership and engineering to productionize models and apply AI-native techniques (LLM-aided EDA, agentic feature engineering, AI-augmented experimentation).

Lead experimentation and causal inference at company-wide scale: define methodology, own statistical approaches for small samples and clustered data, apply causal methods when experiments arent feasible, run experiment review, build tooling and curriculum, and partner with analytics and AI/ML teams to ensure decision-grade results.

Lead design, build, and production deployment of generative AI products (LLM apps, agents, copilots, RAG/search). Rapidly prototype features, build APIs/services and retrieval pipelines, optimize performance, ensure responsible AI, and mentor team members while setting AI technical direction across the organization.

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.

Lead quantitative analysis and modeling of large real-world health datasets to estimate population-level disease prevalence and incidence. Design and implement statistical and machine learning models, maintain data pipelines and dashboards, ensure data quality and reproducibility, collaborate with clinicians and public health experts, prepare scientific reports, and support team onboarding and training.

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.

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 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.

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

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.