Top Senior Analysis Reporting Jobs in Salt Lake City, UT

As a Senior Data Scientist, you will analyze product usage, develop frameworks for user behavior, build statistical models, and collaborate with cross-functional teams to derive actionable insights and influence decision-making.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

Lead and grow a data science team to optimize lending funnels, run experimentation and statistical analyses, build reports/dashboards and data marts, partner with Product/Engineering/Finance, and translate insights to senior leadership to inform product decisions.

The Associate Director will develop credit policies, conduct portfolio analytics, extract data for analysis, and collaborate across functions, influencing decisions and presenting findings to leadership.

Analyze user behavior data to identify friction points, recommend changes for conversion optimization, and collaborate with teams to improve user flows through data-driven insights.

The Senior Data Scientist will develop and deploy analytical models supporting marketing strategies, assess campaign effectiveness, and optimize customer engagement strategies, employing advanced data-driven methods and partnering with marketing stakeholders.

Lead analytics implementations using Google Analytics and Tag Manager for clients, providing mentorship and managing projects with a focus on enhancing clients' digital strategies.

The Senior Data Engineer II will design and maintain scalable data pipelines, collaborate with teams on machine learning workflows, and ensure data accessibility across systems.

The Market Risk Analyst will monitor SBL lines of credit, assess market risks, manage collateral, develop risk models, and prepare reports.

Lead the ML Data Enablement team to define strategy and execution for accelerating third-party and internal data onboarding, enforce data quality and SLAs, build metadata and lineage standards, and deliver scalable data infrastructure and workflows to reduce time-to-production for ML models.