Averis Hiring! Full Time Data Science Intern in Federal Territory - Ricebowl

Data Science Intern

Internship Responsibilities

• Coordinate data and annotation workflows across multiple teams and datasets, gaining exposure to end-to-end operational processes.
• Support data quality management activities, including sampling checks, multi-stage quality reviews, and tracking quality outcomes over time.
• Assist with operations for live data products, including monitoring system health, flagging anomalies, and performing routine checks to ensure continuity of service.
• Prepare and maintain operational dashboards to surface key metrics, workflow statuses, and quality indicators for internal stakeholders.
• Gather and clarify requirements from stakeholders, helping translate them into workflow steps, acceptance criteria, and supporting documentation.
• Support issue tracking and resolution, including first-level triage, escalation coordination, and follow-up to closure with relevant teams.
• Maintain operational documentation, including standard operating procedures, workflow guides, and process notes to support consistent team execution.
• Assist in preparing regular operational and quality reports for stakeholders, highlighting risks, bottlenecks, and opportunities for improvement.
• Collaborate with machine learning and engineering teams to ensure annotation outputs meet agreed specifications and delivery timelines.
• Contribute to continuous improvement initiatives aimed at simplifying processes, enhancing tooling, or reducing operational turnaround times.

Internship Requirements

Education

• Currently pursuing a Bachelor’s degree in Information Systems, Data Analytics, Computer Science, Engineering, or a related field.

Core Skills

• Strong organizational skills with excellent attention to detail, capable of managing multiple tasks concurrently.
• Clear and professional written communication, including documentation and structured stakeholder updates.
• Analytical mindset with the ability to interpret operational metrics and identify emerging trends or issues.
• Preferred Tools & Technical Knowledge
• Familiarity with Agile workflows, ticket management, and structured handovers (e.g., JIRA, Confluence).
• Exposure to AI/ML concepts, including datasets, annotation pipelines, or model workflows.
• Experience with annotation platforms such as Label Studio or similar tools.
• Basic proficiency with reporting/dashboard tools (e.g., Power BI, Tableau, Metabase).
• Awareness of quality measurement concepts such as inter-annotator agreement and sampling-based QA/QC.
• Working knowledge of SQL and/or Python for basic data queries and reporting.
• Exposure to cloud platforms such as AWS is a plus.