DeepSense works with ocean companies to address their data needs and sets up a student project based on one of the four types. Once we have a project developed, we search our Student Pool and interview the best candidates. Throughout the project, students will be supported by experts at DeepSense.
All positions are paid! An internship can last from 4 to 6 months, depending on the company needs. The positions can be full time or part time and remote or on location, depends on the company.
Join the Student Pool (bit.ly/DS-StudentPool ) and send your resume to email@example.com.
The program is open all year round, with flexible start dates.
Please note, only those selected for an interview will be contacted.
Students from all programs are welcome to join, coding experience is not required, just a passion for data, the willingness to learn, and enthusiasm to grow!
DeepSense and the companies will decide on a project in advance. All projects will fall in on of the four categories: Data Analytics, Data Collection and Storage, Data Visualizations, and Data Cleaning and Processing.
Provide actional support to improve decision-making by identifying patterns and relationships with data. Take advantage of the company or third-party data to identify trends, uncover opportunities, predict actions, triggers, or events or assist in enhancements, improvements, or optimization.
Evaluate current and future data collection and storage needs. Identify, assess and evaluate gaps. Develop a plan for scalable data collection and storage environments. Create documentation to add business context to data and augment analysis, e.g data dictionary and SOPs. Facilitate annotation required during the data collection process
Create unique and valuable visualizations to enhance operational or marketing dashboards. Engage with stakeholders to understand how data can be used to augment all aspects of operations. Ensure optimal platforms are leveraged for supporting visualizations (e.g. hosting data, creating dashboards in Tableau or Excel, creating infographics in Canva).
Evaluate the accuracy and quality of data. Identify gaps or barriers to future quality data. Create processes to enhance data collection and future processing. Develop methods and standards to improve data processing in real-time to augment future data analysis. Complete data processing such as fixing or removing incorrect or incorrectly formatted, duplicate, incomplete or outliers within the data repository.
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