Top Entry Level Data Science Jobs in Miami, FL

Cypress Financial seeks a Data Scientist to analyze data for decision-making within lead acquisition and marketing functions. The role involves developing scoring models, reporting, and advanced analytics to improve product strategies and marketing efficiency. Strong skills in Power BI, SQL, and Azure ML are required.

The Data Scientist 2 role at Comcast involves optimizing the performance of syndicated products, engaging with global telecommunications providers, and ensuring operational excellence across the product portfolio. It requires collaboration within a motivated team focused on enhancing customer experiences in digital media and connectivity.

As a Data Scientist at Zelus Analytics, you will perform data modeling and quantitative analysis, develop and validate statistical models, collaborate with engineering for model implementation, and support research projects aimed at enhancing player and team performance.

The Performance Science Data Engineer role involves utilizing data science techniques to enhance live entertainment experiences, focusing on analyzing performance metrics and improving overall event execution. The position may collaborate cross-functionally to drive data-driven decisions and optimize audience engagement.

As a Data Scientist at Horizontal Digital, you will develop algorithms and predictive models to solve business challenges for Salesforce, enhance CRM applications, and create valuable insights through data analysis and visualization. You will design data models and manage data as a product while collaborating with various teams to deliver analytics and dashboards.

The Field Application Scientist role at Cytiva involves providing technical support to sales teams and customers, managing leads, conducting training, and enhancing account relationships in the Nano Medicine sector. The position requires developing product knowledge and contributing to team goals within a remote work environment.

As a Data Engineer at Archive, you will build data pipelines, develop AI use cases, architect data infrastructure, and improve reporting practices. You will work with large datasets and collaborate with various teams to solve complex problems and enhance product metrics.