Operations Specialist (Data Ops & Project Management)

Operations Specialist (Data Ops & Project Management)

Location:

Team:

Employment:

Job Scope:

Handles the hands-on execution of data annotation and data collection across image, text, audio, and video. Owns and/or drives project scoping, costing, and planning in addition to delivery. Ensures quality, efficiency, and integrity of pipelines sourced from external datasets, vendors, and general population contributors. Oversees timelines, quality, and cross-team coordination for both data-centric and Applied AI projects.

Key Responsibilities:

Data Operations

• Execute data annotation tasks (bounding boxes, transcription, classification, etc.).
• Set up and manage crowd-based data collection campaigns (e.g., voice, selfies, typed prompts).
• Scrape or source data from online repositories or public datasets as required.
• Validate, clean, and package data before handoff to AI teams and external clients.
• Conduct internal QA checks on collected and annotated data.
• Report and drive edge cases and anomalies to PMs and AI leads to an aligned resolution.
• Onboard, guide, and support freelancers or contributors.
• Manage vendors/third parties (onboarding, SLAs, quality, invoicing, compliance).
• Develop training materials and run calibration (guidelines, gold sets, certification).
• Track throughput, accuracy, rework, and SLA performance.

Domain Areas:

• Annotation execution and quality control
• General population data collection setup and support
• Online/offline data sourcing (scraping, APIs)
• Data cleaning, packaging, and schema validation
• Contributor and vendor management
• Training, guidelines, and calibration (IAA)
• Cross-functional coordination (Tech, R&D, Product, Commercial)
• Project scoping, costing, planning, and delivery tracking
• SLA tracking, reporting, and escalation management
• Metrics tracking (completion rate, quality, volume, SLAs)

What we offer:

• High ownership in a fast-growth startup: ship weekly, see impact immediately, and shape playbooks used across clients.
• Clear growth paths: individual contributor and lead tracks, with defined competencies and regular progression reviews.
• Cross-pillar exposure: rotations and joint projects with Applied AI and Tictag Insight (retail CV analytics).
• Mentorship and coaching: weekly 1:1s, shadowing PMs/AI leads, and access to senior reviewers for complex edge cases.
• Lead opportunities early: own pilots, run vendor QBRs, mentor junior annotators, and author guideline releases.
• Remote work: focused on collaboration days for calibration, training, and vendor reviews.