Project Perseus | Data Quality Analyst - Arabic Speakers (Human-in-the-Loop AI)

Data Quality Analyst

Welo Data is looking for experienced, detail-oriented professionals to join our team as Data Quality Analysts. This role sits at the center of execution and quality — bridging Data Labeling Associates (DLAs) and Team Leads to ensure work is not only completed, but done right.

You'll work closely with both people and AI systems — auditing outputs, supporting day-to-day execution, and helping teams apply guidelines correctly in fast-moving, real-world scenarios. The work combines data quality, light project coordination, and hands-on training, where your ability to guide others and think critically is just as important as your own output.

Project Details

• Job Title: Data Quality Analyst
• Hiring in: NYC, Seattle, Bellevue, Redmond, San Francisco, Sunnyvale, Burlingame, Austin, Los Angeles, Washington DC, Chicago, Boston
• Hours: Full-time, 40 hours per week
• Employment Type: W2 Full-Time Employee
• Work Authorization: Must be authorized to work in the U.S. (no visa sponsorship)
• Pay Rate: $38/hour
• Contract Duration: 1-year contract with possibility of extension

Important: This is a 100% onsite position — remote work is not available for this role. To be considered, candidates must be located in or able to commute to one of the following cities: NYC, Seattle, Bellevue, Redmond, San Francisco, Sunnyvale, Burlingame, Austin, Los Angeles, Washington DC, Chicago, Boston. Please only apply if you meet this location requirement.

What You'll Do

• Support quality and execution across DLA teams, ensuring work meets defined standards at scale
• Audit DLA outputs and provide structured, actionable feedback to improve accuracy and consistency
• Act as the first line of support for DLAs — answering questions and helping interpret guidelines
• Help DLAs navigate ambiguity and apply evolving instructions effectively
• Support onboarding and training of new DLAs through hands-on guidance and coaching
• Monitor workflows, queues, and blockers — escalating risks and gaps to Team Leads
• Identify patterns, recurring issues, and edge cases in both human and model outputs
• Participate in calibrations, team discussions, and stakeholder syncs
• Contribute to improving guidelines, processes, and overall team performance
• Document findings and feedback in a clear, concise, and actionable way

What We're Looking For

• Native-level language proficiency and a university degree (Bachelor's or higher).
• B2 or superior level of English.
• 2–4 years of experience in data annotation, content quality, QA, or related fields
• Strong ability to interpret and apply complex guidelines with consistency
• Excellent attention to detail with a high bar for quality
• Ability to stay consistent while working with evolving guidelines and priorities
• Experience in AI/ML data workflows or human-in-the-loop evaluation environments
• Prior experience auditing or reviewing the work of others
• Familiarity with safety, compliance, or policy-driven content evaluation

Benefits

• Paid Vacation: 6 days
• Paid Company Holidays: 2 days (Memorial Day and Labor Day)
• Paid Sick Leave: accrued per applicable state law and company policy
• Medical, Dental, and Vision Insurance (eligibility applies)
• Health Savings Account (HSA)
• 401(k) Retirement Plan
• Employee Assistance Program
• Additional voluntary benefits (life, accident, critical illness, etc.)
• Free Gourmet Food: Free breakfast, lunch, and dinner are provided, featuring a wide variety of cuisines in multiple cafes.
• Micro-kitchens & Snacks: Offices are stocked with free snacks and beverages, including premium coffee and La Croix.
• Unique Campus Features: Some locations include roof-top nature parks
• Commuter Benefits: Free transport, shuttles, and sometimes bike-to-work perks.

$38 - $38 an hour Why This Role This is an opportunity to move beyond traditional data work and play a direct role in how AI systems are evaluated and improved. The work is fast-moving, collaborative, and increasingly central to how modern AI systems are built and deployed.