As a Fisheries Data Labelling and Annotation Intern at OnDeck Fisheries AI, you will have the opportunity to work at the intersection of fisheries science and technology. This internship is designed for fisheries students who are passionate about contributing to the sustainable management of aquatic ecosystems through innovative AI solutions.

Key Responsibilities Include:

1. Data Labeling: You will be responsible for labeling and annotating fisheries-related data sets, including images, videos, and sensor data. This task is crucial for training machine learning models used in fisheries monitoring and assessment.
2. Scientific Research, Reporting, and Analysis: Alongside data labeling, a significant part of your role involves conducting scientific research, reporting, and analysis on various aspects of fishing activity. This includes analyzing data from the videos you are labeling and leveraging your domain-specific knowledge to provide insights
3. Quality Control: Ensure the accuracy and quality of labeled data by conducting regular checks and validations. Collaborate with the data science team to refine labeling guidelines.
4. Documentation: Maintain clear and comprehensive documentation of the labeling process, guidelines, and any issues or challenges encountered.
5. Collaboration: Collaborate with other interns, and with our team of fisheries experts, Software Engineers, and Machine Learning Engineers, to exchange knowledge and insights. Participate in team meetings to discuss project progress and share ideas.
6. Feedback Integration: Provide feedback on the labeling process, suggesting improvements and optimizations to enhance efficiency and accuracy.

This student should be:

• Currently enrolled in a fisheries science, marine biology, or related program, or have experience as a fisheries observer.
• Strong interest in fisheries management and technology.
• Attention to detail and ability to maintain data accuracy.
• Good organizational and documentation skills.
• Excellent communication and teamwork skills.
• Basic knowledge of data labeling tools and techniques is a plus, but not required.

By the end of this project, there will have been an immense impact on both the company and the student's learning:

1. Enhanced Data Quality: Through the diligent work of the intern in labeling and annotating fisheries data, the quality and accuracy of the labeled data sets are improved. This is crucial for the success of machine learning and data analysis models used in fisheries technology applications.
2. Skill Development: The intern gains hands-on experience in fisheries data management and labeling, which can be a valuable addition to their skill set. They may also develop proficiency in data annotation tools and techniques.
3. Contribution to Sustainability: By supporting the accurate labeling of fisheries-related data, the project contributes to the sustainable management of aquatic resources. Accurate data is essential for informed decision-making in fisheries management, conservation, and policy development.
4. Team Collaboration: The intern collaborates with other team members, including data scientists and fisheries experts. This experience fosters teamwork, communication skills, and the ability to work in a multidisciplinary environment.
5. Documentation Improvement: The documentation of the labeling process is enhanced, ensuring that future team members can benefit from clear guidelines and procedures. This improves the efficiency and consistency of data labeling efforts.
6. Long-term Impact: Accurate labeling of data sets can have a lasting impact on the effectiveness of fisheries management strategies, contributing to the sustainable use of aquatic resources and conservation efforts.