Data Analyst

Aquent is partnering with an innovative and leading technology company at the forefront of AI development. This organization is dedicated to pushing the boundaries of what's possible, creating solutions that impact millions globally. They foster a culture of excellence, collaboration, and continuous learning, where every team member contributes to groundbreaking advancements. Join a team where your work directly fuels the next generation of intelligent systems and shapes the future of technology.

Are you a meticulous and proactive data professional eager to play a pivotal role in the development of cutting-edge AI models? We are seeking a detail-oriented individual to join our dynamic team and ensure the integrity and readiness of critical datasets. In this role, you will be instrumental in managing vast quantities of document and text data, directly impacting the quality and performance of AI model training. Your expertise in data operations, quality assurance, and cloud storage will be crucial in streamlining our OCR processes and maintaining pristine data inventories. This is an exciting opportunity to contribute to significant projects, collaborate with engineers and applied scientists, and see your efforts directly translate into advancements in artificial intelligence.

Key Responsibilities

• Operate and monitor existing OCR workflows, tracking processing queues, job status, and completion rates, while identifying and addressing failed or stalled batches.
• Review logs and processing reports to pinpoint exceptions, execute documented recovery steps, and escalate technical issues to engineering teams as needed.
• Maintain comprehensive batch tracking records, coordinating reruns, prioritizing tasks, and managing outstanding processing activities.
• Conduct thorough reviews of OCR outputs against source documents using sampling and established quality criteria to identify discrepancies such as missing pages, empty outputs, duplicate records, unreadable text, or incomplete content.
• Run and evaluate results from existing cleaning and validation tools, flagging questionable changes for further investigation and resolution.
• Manage issue logs, categorize recurring errors, and track corrections through to their successful resolution, while summarizing quality trends and recommending process improvements.
• Maintain detailed inventories that link source documents, OCR outputs, cleaned data, and various dataset versions for complete traceability.
• Validate critical metadata including document identifiers, titles, subjects, languages, page counts, and processing status to ensure accuracy.
• Reconcile batch manifests and tracking records against actual files to identify and resolve any missing, mismatched, or duplicate items.
• Organize generated data adhering to agreed-upon folder structures, naming conventions, and versioning standards for optimal accessibility and management.
• Prepare comprehensive dataset delivery packages, complete with manifests, quality summaries, and documented exceptions.
• Manage the uploads, downloads, and transfers of source files and generated datasets within Azure storage environments.
• Verify transfer completion, file counts, and data integrity using established tools and reports.
• Investigate and resolve routine transfer failures, incorrect paths, and access issues, coordinating with administrators when necessary.
• Adhere to approved access, retention, and data-sharing procedures, maintaining meticulous records of dataset deliveries and changes.
• Maintain operational trackers and dashboards to provide clear visibility into processing progress, backlog, quality, and dataset readiness.
• Provide concise and timely updates on completed batches, unresolved issues, and potential delivery risks.
• Maintain standard operating procedures and facilitate smooth handoffs between data operations, engineering, and model-training teams.

Required Qualifications

• 2+ years of hands-on experience in data analysis, data operations, data quality assurance, or dataset management.
• Demonstrated hands-on experience with cloud storage, including file organization, uploads, and downloads.
• Proven experience in checking data completeness, accuracy, consistency, and metadata quality.
• Ability to effectively utilize existing dashboards, processing tools, and logs to monitor workflows and investigate exceptions.
• Familiarity with common document and data formats such as PDF, text, CSV, and JSON.
• Strong attention to detail, excellent documentation skills, and clear communication abilities.
• Capacity to consistently manage repetitive validation tasks while effectively prioritizing exceptions and seeing issues through to resolution.

Preferred Qualifications

• Experience specifically with AI training-data operations.
• Familiarity with Azure Storage Explorer, AzCopy, or comparable file-transfer tools.
• Basic SQL skills for querying, validation, and reconciliation tasks.
• Basic Python knowledge for running existing scripts or making minor adjustments (advanced coding is not required).
• Experience with Power BI or similar operational reporting tools for data visualization.
• Familiarity with concepts such as dataset versioning, source traceability, access controls, and human-review workflows.