Top Hybrid Data Analysis Jobs

The Data Strategy & Sourcing Analyst transforms product data into a strategic asset, ensuring data integrity and driving cost savings through sourcing. Responsibilities include establishing a Single Source of Truth, conducting data analysis, and collaborating with teams for financial impact. The role requires strong analytical skills, proficiency in SQL, and experience with data management tools.

The Senior Data Analyst oversees performance reporting tools, automates data pipelines, conducts quantitative analyses, and communicates findings to various stakeholders.

The Data Scientist II will focus on solving analytical problems using statistical methods and machine learning, optimizing food logistics. Responsibilities include developing and deploying models, analyzing large datasets, and communicating insights to stakeholders. Emphasis on operational decision-making and real-world applications in logistics and supply chain.

Monitor merchant and transaction activity to detect fraud, analyze transactional data for risk trends, create and refine detection rules, investigate alerts and chargebacks, collaborate with underwriting/compliance, and improve risk tools and processes.

The Junior Data Analyst will conduct qualitative and quantitative analysis, evaluate processes, and provide scheduling and documentation support for a DHS client.

Collect, clean, normalize, and analyze complex datasets; develop data collection systems; maintain databases; create queries, views, and reports; generate ongoing reports and identify process improvement opportunities.

As an Analyst, you will develop asset-level forecasts and models, analyze power market data, and collaborate with clients to provide insights on North American power markets.

The Staff Data Scientist will drive growth in marketing by analyzing data, building models, and creating frameworks for marketing measurement, optimizing spending, and supporting decision-making across teams.

The Marketing Science Analyst will analyze consumer data, maintain statistical models, and collaborate with teams to derive insights for business decisions.

Support audit teams by fulfilling data requests, building analytics dashboards and automations (Power BI, UiPath, Dataiku, Python/R), integrating AI tools, and contributing to financial, SOX, IT, and operational audits while following SDLC and data governance.