Top Senior Level Data & Analytics Jobs in Minneapolis, MN

As a Senior Data Scientist at Discord, you'll partner with teams to analyze and improve user experience through data analytics, A/B testing, and building dashboards. You'll define key metrics, communicate findings to diverse audiences, and collaborate with technical teams to design effective data solutions.

As a Senior Data Scientist, you will lead the development and deployment of machine learning models, mentor junior data scientists, and collaborate with various business teams. You will design data products, ensure model quality, and promote good practices within the data team.

The Product Analyst will analyze large-scale data to improve business performance, focusing on user engagement and conversion rates. Responsibilities include testing strategy implementation, defining KPIs, and creating visual dashboards. The analyst will collaborate with marketing and product teams to ensure data integrity and capture requirements.

The role involves implementing and supporting Credit and Debit Card programs, managing client training, documentation, and GoLive activities. The analyst will configure card functionality, support clients before and after implementation, diagnose issues, and create process documentation while ensuring data integrity during conversions.

The Oracle Cloud Financials Analyst is responsible for working with clients to understand their financial management needs, designing solutions using Oracle Fusion Financials, configuring applications, conducting testing, overseeing data migration, and training users on new systems. Post-implementation support is also a key responsibility.

The Senior Data & AI Specialist at IOTA will lead data analysis, model development using machine learning, and product strategy development. Responsibilities include data visualization, cross-functional collaboration, and process optimization to enhance data management and insights.

The Data Engineer III is responsible for architecting and maintaining scalable tooling and infrastructure for data management, including building automated data pipelines and managing data storage. The role involves collaborating with Data Scientists for model improvements and ensuring data quality and availability, while also coaching teams on data-driven decision making.