Top Data Science Jobs

Develops predictive models, quantitative analyses, data visualizations, and machine learning solutions for Department of Defense clients. Responsibilities include data exploration, cleaning, mining, statistical programming, natural language processing, text mining, dashboard development, and advising stakeholders on actionable insights. Requires experience with Maven Smart System and a TS/SCI clearance.

Analyze structured and unstructured data to develop predictive models, quantitative analyses, algorithms, and visualizations. The role also manages SharePoint workflows, permissions, metadata, retention policies, records governance, shared-drive cleanup, information security controls, and knowledge-management procedures. Candidates collaborate with clients and IT teams, improve information discovery and platform adoption, and require a bachelor’s degree plus TS/SCI clearance with polygraph.

Designs, develops, and programs methods to analyze unstructured, diverse, and large datasets. Develops algorithms and automated processes to evaluate data from disparate sources, generating and communicating actionable insights that support informed business decisions across customer support and equipment lifecycle operations.

Leads Mastercard’s Open Finance data science strategy and global organization. Oversees production-grade AI and machine learning solutions for transaction categorization, entity extraction, fraud detection, credit risk, propensity modeling, personalization, and financial insights. Partners with Product, Engineering, Sales, Risk, Marketing, Legal, and Compliance to commercialize scalable solutions, establish MLOps and governance practices, ensure responsible AI, and grow high-performing data science teams.

Leads Mastercard’s Open Finance data science strategy, products, and global organization. Oversees production machine learning and AI capabilities for transaction categorization, entity extraction, fraud detection, credit risk, personalization, and predictive analytics. Partners with Product, Engineering, Sales, Risk, Marketing, Legal, and Compliance to commercialize scalable, explainable, and responsibly governed solutions. Builds talent, establishes operating standards, drives MLOps and model governance, and advises executives and customers on AI applications.

Develops and deploys complex Generative AI and machine learning solutions across HP projects. Responsibilities include building and monitoring models, defining performance metrics, identifying business insights, collaborating with stakeholders, creating visualizations, ensuring scalable implementation, and leading data science project teams. The role also provides technical guidance, training, and mentoring while working with large language models, multimodal models, RAG, vector databases, and cloud or edge deployments.

Develops data-driven tools, predictive models, AI solutions, and interactive visualizations for electricity forward market operations. Analyzes market funding efficiency, congestion, transmission and generation outage data, and Financial Transmission Rights outcomes. Collaborates with engineers, operators, analysts, and Agile teams to translate enterprise data into business insights, improve market performance, and communicate recommendations to technical and non-technical stakeholders.

Conducts intermediate data analysis, mining, validation, reconciliation, hypothesis testing, and visualization to support business decisions. Develops predictive and prescriptive analytical tools, dashboards, reports, and actionable insights across business areas. Collaborates with stakeholders and technology partners to acquire, manage, and operationalize data. Requires a bachelor’s degree in a quantitative or technical field, two years of relevant experience, and proficiency with SQL, Power BI, business intelligence platforms, and statistical analytics tools.

Develop and enhance client data science capabilities by analyzing complex structured and unstructured datasets, evaluating AI and machine learning models, translating findings into actionable insights, and mentoring teammates. Collaborate with defense and intelligence clients to answer analytical questions, support leadership decisions, and improve data-driven operations.

Lead data science and data engineering efforts, including data pipelines, scalable platforms, analytics, machine learning tools, predictive systems, and statistical analysis. Mentor multidisciplinary teams, integrate structured and unstructured data, develop Python and SQL algorithms, create visualizations, and support mission-focused data platforms. The role requires TS/SCI clearance with CI polygraph, Security+ certification, and substantial experience in data applications, coding, modeling, and platform operations.

Analyze structured and unstructured data, develop algorithms and systems, apply scientific methods, and deliver objective insights to intelligence clients. Collaborate in fast-paced environments to gather, cleanse, parse, and interpret data using statistical and general-purpose programming languages.

Performs advanced analysis and data mining on large structured and unstructured datasets to generate business insights. Develops machine learning algorithms and statistical models to predict outcomes and recommend actions. Conducts analytical experiments for product and process optimization, presents findings through visualization and data storytelling, and collaborates with data architects, analysts, engineers, and visualization experts.

Leads applied RF data science research from mathematical formulation through implementation, experimentation, evaluation, and release recommendations. Develops signal detection, classification, estimation, uncertainty, and structured event-description techniques using measured, synthetic, and hybrid data. Builds reproducible datasets and evaluation pipelines, diagnoses failures, qualifies techniques, and collaborates with RF/DSP engineers, runtime developers, analysts, and field teams. Requires Python proficiency and the ability to review C++/CUDA implementations. Travel up to 10% and security-clearance eligibility are required.

Analyzes and models data to generate insights that improve client campaign performance. Responsibilities include data extraction and transformation, statistical analysis, predictive modeling, data visualization, client presentations, project delivery, stakeholder collaboration, and development of standardized analytical procedures. The role also supports marketing research, value-added analyses, AI-driven solutions, sales materials, and code collaboration using version-control tools.

Lead data science efforts for a defense program onsite in Hazelwood, MO. Define data requirements, build analysis systems, design/deploy ML models, collaborate with engineers and SMEs, mentor junior scientists, ensure security/compliance, and communicate results to stakeholders while improving models and establishing best practices.

Develop advanced data science solutions for Intelligence Community missions, including scalable data pipelines, automation, data models, visualizations, dashboards, and analytic applications. Apply Python and advanced analytics to large, complex datasets, identify actionable patterns, modernize workflows, and support operational decision-making. Collaborate with intelligence professionals, manage multiple projects independently, and deliver maintainable data products aligned with enterprise modernization initiatives.

Analyze complex datasets to deliver actionable insights for production, resource allocation, and personnel decisions. Develop Tableau dashboards and visualizations, preprocess and clean structured and unstructured data using Python or R, and analyze time-series data. Collaborate with stakeholders, ensure data quality, document methodologies, and provide recommendations. The role requires an active TS/SCI with Polygraph clearance and is onsite in Northern Virginia.

Data Scientist Interns analyze complex datasets, define metrics, conduct experiments, identify trends, and develop actionable recommendations for PayPal products. They collaborate with product, engineering, and data teams, improve data quality, communicate insights to technical and non-technical stakeholders, and contribute to end-to-end machine learning solutions while managing multiple projects.

Lead the end-to-end development and deployment of advanced machine learning models for marketplace risk and ecommerce challenges. Responsibilities include data-source validation, exploratory analysis, feature engineering, model evaluation, scalable ML architecture, stakeholder consultation, and translating business needs into measurable data strategies. The role also requires mentoring technical team members, applying emerging AI techniques, and driving reliable, high-impact data science solutions.

Perform exploratory data analysis, statistical research, machine learning, predictive modeling, and visualization to generate actionable business insights. Own governance, adoption, and integration of a global supply chain digital tool, serving as the SIOP point of contact. Translate operational needs into technical requirements, improve data quality, coordinate stakeholders, drive change management, and partner with digital technology teams across a matrixed organization.

Develop and execute AML transaction monitoring data strategy, build and monitor model performance metrics, investigate data quality issues, define transaction-monitoring specifications with business partners, and address open AML findings to ensure effective anti-financial crime controls.

The Data Scientist, Marketing Analytics develops predictive models for customer lifetime value, acquisition propensity, churn, attribution, and marketing mix optimization. The role designs campaign experiments, builds data pipelines integrating advertising, web, and sales data, creates customer segments, and communicates insights through executive-level visualizations. It partners with marketing, media, product, data engineering, and analytics teams while supporting privacy-compliant data practices and marketing personalization.

Develop and deploy LLM and RAG solutions for utility and industrial operations. Responsibilities include fine-tuning and evaluating models, building Python integrations with vector databases, analyzing unstructured operational data, deploying models in Azure or AWS, and collaborating with engineering and field operations teams.

Build and implement machine learning models for personal loan acquisition, including customer selection, campaign optimization, underwriting, pricing, and verification. Partner with data scientists, engineers, and product managers; analyze large-scale numeric and textual datasets using Python, Spark, AWS, and related tools; validate and backtest models; and translate statistical insights into customer-focused business outcomes.

Conduct bioinformatics and data science analyses to identify and prioritize vaccine targets. Develop comparative genomics, phylogeny, functional prediction, and omics analysis methods; build pipelines, databases, and dashboards; interpret findings; and communicate results to computational and experimental teams. Coordinate cross-functional Vaccines R&D projects and contribute to scientific publications, reports, and presentations.