Data Specialist Machine Learning Data Operations

Data SpecialistRequired Qualifications - Excellent communication and interpersonal skills and capacity to ask questions when instructions are not clear enough - Highly organized with strong attention to detail - Strong time management, and able to juggle multiple projects under tight deadlines - Self-motivating with the ability to work independently and with little supervision - Proficiency with macOS, iOS and iPadOS, and with Client apps (Mail, Calendar, Numbers, Keynote) - Consistent quality and speed on repetitive tasks - Ability to independently learn and navigate systems and tools - Able to work on-site in Cupertino 3-5 days per week - Comfort organizing, validating, and spot-checking data files (renaming, reformatting, basic spreadsheet logic)Preferred Qualifications - 3+ years in a professional setting, ideally in data collection, or user study data operations - Experience configuring annotation or customizing data collection tools - Experience with moderating in-person data collection or user studies - Basic knowledge of running cli commands in terminal, and working with python scriptsDescription Our Machine Learning Data Ops team builds high-quality datasets at scale to train and evaluate Client AI and ML models. We're looking for a contract Data Specialist to support our Data Program across the full data lifecycle. You'll work hands-on in data collection, annotation, processing, tooling and QA, with flexibility to move between programs as priorities change.

Working Style Fast-paced and changing priorities. You'll work independently with light supervision and collaborate closely with Data Program Managers and QA Leads.Responsibilities - Moderate and setup on-site data collection: Run in-person collections, including recruiting participants, logistics, lab setup, device handling and session support - Annotate pilot projects: Classify, draw bounding boxes, and give subjective ratings on text, image and video assets. - Flag trends and edge cases you see in the data or the tool - QA: Review in-house and vendor-delivered data against guidelines.

Score it, give clear feedback, and report issues - Process and packaging datasets: Prepare, organize, rename, convert and validate data assets, and keep deliveries accurate and traceable - Setup and test collection and annotation platforms/devices: Configure projects, tasks and guidelines on platforms/devices. Test them before launch, and troubleshoot as needed**Only those lawfully authorized to work in the designated country associated with the position will be considered.****Please note that all Position start dates and duration are estimates and may be reduced or lengthened based upon a client's business needs and requirements.**