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Digital Divide Data
Computers + 1 more
Manager, Operations (Machine Learning, Autonomous Vehicles & ADAS)
Closed for applications
Description
Education & Experience
- Bachelor’s degree in Data/AI, Computer Science, Engineering, Information Systems, or related fields.
- A minimum of 2.5 years of experience in AI/ML operations, project management, or technical workflow coordination.
- Hands-on exposure to annotation workflows: 2D/3D CV, LiDAR, ADAS, or AV datasets.
- Strong track record managing projects in KPI-driven environments.
- Must have worked in a BPO
- Familiarity with annotation tools such as CVAT, SuperAnnotate, and Labelbox.
- Understanding of ML metrics, data quality principles, and AV/ADAS ecosystems.
Responsibilities
Client Relationship & Communication Excellence
- Build trusted relationships with ML, AV, and ADAS clients to ensure seamless service delivery.
- Understand and articulate project scope, deliverables, timelines, and ownership.
- Serve as the primary liaison for all client requests, updates, and issue resolution.
- Track, manage, and close client requests with clarity, urgency, and professionalism.
- Ensure workflows and outputs fully align with client expectations and technical guidelines.
Operational Delivery Ownership
- Oversee day-to-day execution of annotation, QA, audits, and reporting activities.
- Translate technical guidelines into clear, actionable workflows for delivery teams.
- Monitor team adherence to SLAs/KPIs: accuracy, throughput, productivity, and latency.
- Lead real-time issue resolution and ensure teams maintain context and operational readiness.
- Maintain strict version control of instructions, guidelines, and workflow updates.
Performance Tracking & Continuous Improvement
- Track performance trends across ML/AV/ADAS datasets using scorecards and dashboards.
- Diagnose quality or productivity gaps and implement root-cause fixes.
- Partner with QA and Training teams to refine workflows, conduct refreshers, and clarify instructions.
- Lead performance reporting to clients, highlighting insights, actions, and operational improvements.
Team Leadership & Talent Development
- Mentor and develop teams handling AI, CV, 3D, or LiDAR datasets.
- Build a culture of feedback, technical excellence, and continuous learning.
- Support team decision-making on ambiguous, complex, or escalated annotation scenarios.
- Advise on capacity planning, calibration cycles, and training needs.
Risk Management & Issue Mitigation
- Identify risks related to workflow complexity, guideline ambiguity, tooling inefficiencies, or data quality concerns.
- Develop mitigation strategies to ensure delivery continuity and client satisfaction.
- Support Business Continuity Plans (BCP) and drive readiness for activation.
- Escalate advanced risks to senior leaders and product teams for resolution.
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