Top Remote Data & Analytics Jobs in Phoenix, AZ

Lead a team of ~7 data scientists to deliver timely, accurate product analytics and experimentation insights; manage people, mentor staff, partner with product and engineering, and build accessible data products and measurement practices.

Design, build, and operate large-scale data pipelines and ETL processes to normalize varied partner datasets. Lead technical decisions, mentor engineers, implement testing and monitoring, manage data warehouses/lakes, and collaborate cross-functionally to deliver robust data infrastructure that supports ML and product teams.

Inovalon seeks candidates to join their Talent Community, emphasizing data-driven solutions to enhance healthcare outcomes and economics.

The Senior Enterprise Performance Analyst will develop dashboards, analyze performance data, support decision-making, mentor junior analysts, and improve organizational performance.

Develop and maintain datasets for visualization and machine learning; automate data pipelines; partner with teams to build data products.

Own end-to-end analysis of payments, chargebacks, and user lifecycle to identify drivers of net loss. Deep-dive into fraud cases, design and evaluate A/B experiments and risk-rule changes, build monitoring dashboards, partner with Risk, Payments, and Product to convert insights into mitigations, and run ad-hoc analyses of emerging loss trends.

Lead product analytics: build and maintain dbt models and Looker dashboards, define KPIs, analyze funnel and user journeys, design and evaluate A/B tests, size roadmap impact, and deliver partner and ad-hoc insights to inform product and executive decisions.

The role involves building and scaling a Closed-Loop Design Program that enhances pre-sales engineering through data science and analytics in supply chain automation.

Design, build, and maintain scalable batch and streaming data pipelines, improve data modeling and governance, contribute to enterprise data architecture, and collaborate with cross-functional teams to ensure reliable, high-quality data for analytics and operational use.

Build, operate, and optimize large-scale data pipelines and warehouse schemas. Own Airflow orchestration, AWS-based data security (Lake Formation), automated data quality, and production incident resolution. Partner with stakeholders, mentor engineers, and drive DataOps best practices for performance, cost efficiency, and governance.