We are seeking a detail-oriented data annotator to join the Oracle Analytics team. As a member of our data operations team you will be responsible for creating and annotating natural language and structured data to facilitate the development of new AI solutions for Oracles Analytics product portfolio. You will work with applied science and data science teams to design and evaluate new AI capabilities, test systems ahead of deployment, and support customers with implementing and customizing AI solutions for industry verticals or specific use cases. The ideal candidate will have in-depth familiarity with Oracles analytics products (Fusion Data Intelligence, Oracle Analytics Cloud), SQL, Python, and data visualization, as well as experience in subject matter areas related to ERP, Finance, Supply Chain Management, or Human Capital Management. Knowledge of data collection management practices, quality control processes is a strong plus.
Career Level - IC2
Key Responsibilities:
- Collaborate with applied scientists and product managers to gather data requirements.
- Help develop and improve data annotation guidelines and quality control mechanisms.
- Produce and deliver data meeting predefined quality targets and timelines.
- Help test and evaluate AI solutions.
- Partner with customers and solution architects to improve usability of AI products.
Required Qualifications:
- Bachelors degree in Business Administration, Information Systems, Data Science, Linguistics, or a related field.
- 2-3 years of industry experience in data operations, data annotation, business analytics, or a related role.
- Experience with Oracle Analytics tools and dashboards, SQL, and general task management tools (e.g., Jira).
- Excellent communication and collaboration abilities.
- Attention to detail, sense of ownership, and customer-focused mindset.
- Software engineering skills (SQL, Python).
Preferred Qualifications:
- Knowledge of machine learning concepts and how annotated data supports model development.
- Previous experience with large-scale data collection/annotation and data quality control processes.