Top Hybrid Data Analysis Jobs

Analyze complex datasets, automate and improve business processes, create Tableau dashboards, and deliver reporting insights. The role uses Alteryx, Python, and SQL for data preparation and analysis, performs exploratory analysis and data-quality validation, documents data logic, and collaborates cross-functionally on strategic and operational initiatives.

Lead global thematic research across supply chains, advanced manufacturing, defense, aerospace, energy, strategic technologies, and pharmaceuticals. Analyze industry fundamentals, policy, geopolitics, market signals, and quantitative indicators; produce commercial research and actionable insights; collaborate across banking research and business teams; communicate findings through written reports and client engagements.

Lead quantitative and qualitative market research across customer panels, brand tracking, thought leadership, and advertising concept testing. Manage research vendors, survey platforms, participant recruitment, data quality, and panel operations. Synthesize findings into executive-ready recommendations and data stories for marketing, product, and communications teams. Build reusable research frameworks, improve ResearchOps workflows, support data governance, and provide light mentorship to junior team members.

Leads data analysis strategy, modeling, reporting, experimentation, and process improvement using large and complex datasets. Manages and mentors junior analysts, collaborates across business units, supports business intelligence initiatives, and contributes to data products, dashboards, testing frameworks, and operational efficiency improvements. The role involves programming, database analysis, strategic project ownership, and stakeholder consultation in a hybrid environment.

Leads enterprise analytical tool validation, assessing methodology quality, technical risks, usage, and opportunities. Plans validation projects, researches emerging analytical practices, prototypes solutions, communicates findings through reports and presentations, and mentors junior staff. The role requires extensive data analysis, open-source data technology development, statistical or econometric modeling, large-scale data analysis, governance, predictive analytics, and people management.

Analyze complex data, build self-service data solutions, develop business intelligence dashboards, and deliver strategic insights. Manage data quality, metadata, lineage, access governance, and monitoring. Partner with business and technology teams while using Python, R, Spark, SQL, AWS, and visualization tools. The role requires substantial data analytics, scripting, querying, business intelligence, data governance, and process improvement experience.

Analyze how customers use Hi Marley to guide product, customer success, and company decisions. Define feature success metrics, evaluate adoption funnels, measure pilots and utilization, present customer-facing insights, and establish trusted self-service reporting. Partner with Data Engineering to translate analyses into governed data models and communicate findings clearly to executives and customers.

Supports Finance and cross-functional teams by building data pipelines, improving data quality and governance, automating reporting, developing Power BI dashboards, applying AI and analytics, and contributing to financial models, forecasts, reconciliations, and executive insights.

Leads advanced geospatial analysis and modeling for operational requirements, mentors GIS staff, defines the geospatial roadmap, manages geodatabases and QA/QC, produces cartographic materials, develops Python and ModelBuilder automation, and supports cross-functional contract needs. Ensures compliance with data governance, security protocols, ITAR, and program-specific data handling requirements while using Esri GIS platforms and related spatial technologies.

Leads AI-enabled pricing and commercial excellence initiatives for John Deere Construction & Forestry. Applies analytics, competitive intelligence, pricing models, and scalable processes to improve deal quality, decision speed, revenue, and margins. Partners with pricing, sales, marketing, finance, revenue management, dealers, and leadership to translate complex data into actionable guidance and drive adoption of advanced pricing capabilities.

Develop sophisticated investment and trading models using statistical techniques, machine learning, and real-world datasets. Conduct end-to-end quantitative research, test investment hypotheses, collaborate with engineers, analyze market behavior, and communicate complex findings. Participate in academic seminars, reading groups, and industry conferences focused on statistics, machine learning, and data science.

Investigate Terms of Service and Community Guidelines violations, identify abuse trends, and develop automated detection and enforcement rules. Analyze large datasets using SQL, create dashboards and reports, measure mitigation effectiveness, and recommend policy, process, and product improvements. Collaborate with Product, Engineering, and scaled review teams, apply LLM-based approaches, provide training, and improve internal investigation tools and safety operations.

Analyze revenue performance, forecasting, business drivers, and market trends to support planning and commercial growth decisions. Partner with Sales, Finance, Business Intelligence, and other stakeholders to identify risks, opportunities, and actionable insights. Develop segmentation, opportunity-sizing, prioritization analyses, and investment business cases. Improve recurring reporting and planning processes through automation and scalable tools, translating complex data into clear recommendations for senior leadership.