Top Remote Data & Analytics Jobs in Minneapolis, MN

As a Sr. Data Analyst, you'll analyze financial data in structured finance, collaborate with engineering, and provide insights to clients in the industry.

Oversee quality assurance for variant interpretation, lead a team of scientists, ensure adherence to guidelines, and support clinical deliverables.

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

The Research Analyst will support market research projects by designing surveys, analyzing data, and collaborating with clients and teams to ensure project delivery.

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

As Measurement Lead, you will manage high-stakes customer discussions about campaign results, write SQL for data analysis, build dashboards, and collaborate with various teams to enhance Kard's measurement approach.

Lead the design and operation of Twilio's ML and data platform, architecting cloud-native pipelines and implementing MLOps best practices while mentoring engineers and collaborating across teams.

The Director of Data Strategy and AI leads data and AI initiatives to enhance analytics programs, drives client strategy, and ensures effective AI product deployment. Responsibilities include managing cross-functional teams, supporting new business development, and overseeing technical infrastructure for analytics and internal tools while facilitating communication between technical and business stakeholders.

The Senior Data Analyst, SEO will analyze SEO performance data, identify trends, and provide insights for product strategy and marketing. Responsibilities include reporting, benchmarking, KPI standardization, and collaborating with various teams on SEO measurement and improvement.

As a Sr. Engineering Insights Analyst, you'll analyze engineering data, create dashboards, and drive insights to improve engineering processes, productivity, and reliability. Collaboration with engineering teams is key to defining metrics and ensuring data quality.