Surgical Video Data & Annotation Operations Manager (Pune)

Codvo Private Limited; Pune District; On-site; INR 1,200,000 - 1,500,000; Full time

Job summary

A tech-driven data solutions company in Pune is looking for a Surgical Video Data & Annotation Operations Manager. The role involves overseeing the ingestion, annotation, and delivery of over 10,000 hours per month of surgical video with a focus on quality and automation. Candidates should have experience in project management, data quality assurance, and familiarity with annotation tools like CVAT and MONAI Label. Join a collaborative team striving for innovation in data processing.

Qualifications

• Experience in managing large-scale video data annotation projects.
• Strong understanding of data quality and assurance practices.
• Proven track record with automation in data processing.

Responsibilities

• Own and manage a production pipeline for surgical video data.
• Define guidelines and ontologies for annotations.
• Integrate tools for automation and quality management.

Skills

Video data management

Quality assurance

Data annotation

Automation

Project management

Tools

CVAT

MONAI Label

FiftyOne

Job description

Title: Surgical Video Data & Annotation Operations Manager

Location: Pune

About Us

At Codvo, we are committed to building scalable, future-ready data platforms that power business impact. We believe in a culture of innovation, collaboration, and growth, where engineers can experiment, learn, and thrive. Join us to be part of a team that solves complex data challenges with creativity and cutting‑edge technology.

Role Summary

Own and scale a production pipeline that ingests, de‑identifies, annotates, and delivers 10,000+ hours/month of surgical video with ≥70% automation and audit‑ready compliance. You will design the ontology, stand up model‑in‑the‑loop labeling, run QA/IAA, and hit SLAs from ingest → labeled → approved datasets for training and evaluation.

Core Responsibilities

1) Program Ownership & Throughput

• Deliver ≥10,000 labeled video hours/month within SLA (≤7 days ingest→approved).
• Maintain ≥70% auto‑accept rate via model‑assisted labeling; drive to 80%+ over time.
• Operate daily pipeline: ingest → de‑id → pre‑labels → annotation → QA → release.
• Manage backlog, staffing, and shift planning to sustain 333+ hours/day throughput.

2) Ontology & Guidelines

• Define and version surgical ontologies: phases, steps, events, tools, anatomy, quality flags.
• Author annotation guidelines with boundary rules and ambiguity handling.
• Run change control and backward compatibility across dataset versions.

3) Tooling & Automation

• Stand up and operate CVAT (or equivalent) with API‑driven workflows.
• Integrate MONAI Label (or similar) for model‑assisted segmentation/active learning.
• Use FiftyOne (or equivalent) for dataset QA, error analysis, and sampling.
• Implement propagation (tracking/interpolation) and confidence‑based routing.
• Partner with ML to deploy pre‑label models (tool detection, phase recognition, SAM‑style masks).

4) Quality, IAA & Release Gates

• Define gold sets and acceptance thresholds:
• Phase/event F1 ≥ 0.92
• Tool mAP ≥ 0.85
• Anatomy Dice ≥ 0.85
• Run double‑labeling (≥10%), adjudication and weekly QA reviews.