Project Manager (Machine Learning, Autonomous Vehicles & ADAS) at Digital Divide Data

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.




What You'll Bring

Education & Experience


Bachelor's degree in Data/AI, Computer Science, Engineering, Information Systems, or related fields.
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.


Technical & Domain Knowledge


Familiarity with annotation tools such as CVAT, SuperAnnotate, and Labelbox.
Understanding of ML metrics, data quality principles, and AV/ADAS ecosystems.