Top Hybrid Data Analysis Jobs

The Senior Data Scientist will lead analytics projects, define success metrics, run experiments, and present data-driven insights to influence product and business strategies.

The Senior Data Analyst for Payments will analyze and optimize payment processes, collaborate with cross-functional teams, and ensure regulatory compliance to improve financial operations.

Lead analytics strategy and decision support across Product, Growth, Revenue, Operations, and Finance. Perform deep-dive analyses, design experiments, build predictive models and ML/AI prototypes, create trusted reporting and dashboards, ensure data quality, automate workflows, and partner with stakeholders to drive measurable business outcomes. Act as a primary analytics partner, prioritize work, communicate recommendations, and mentor analysts informally.

Lead analytical work to monitor loan portfolio and originations, detect credit and underwriting issues, build multivariate statistical models, analyze funnels, quantify costs/benefits, present recommendations to executives, design monitoring and controls, and drive implementation using SQL, Python, Excel, AI tools, and financial/statistical techniques.

As a Pricing and Revenue Planner, you will manage pricing analytics, forecasting, synthesizing data into actionable insights, and collaborating with various teams to drive revenue improvements and profitability analysis.

The Director of Biostatistics leads a team in clinical research, developing statistical plans, collaborating across departments, and ensuring regulatory compliance.

Lead product data analytics: analyze large datasets, build models and dashboards, design and evaluate A/B tests, advise product decisions, automate analytics with AI-enabled tools, and collaborate with global R&D and product teams to deliver actionable insights.

Lead data analysis projects to help merchant partners understand Upside's impact, manage analytics relationships, and improve product strategy.

Own the Palantir Foundry platform from a finance perspective, developing data pipelines, integrations, ontologies, applications, and interactive dashboards. Troubleshoot data and system issues, document reverse ETL flows and technical specifications, and improve business processes through automation. The role requires experience with data visualization, Databricks or similar platforms, PySpark, TypeScript, NetSuite integrations, and data analysis.

Evaluates analytical and fraud models, compares performance, prioritizes models for implementation, and coordinates deployment into production. Manages timelines, requirements, dependencies, deliverables, and issue resolution across data science, technology, modeling, and business teams. Tracks model performance before and after implementation, analyzes results, prepares reports, and communicates recommendations to stakeholders.