Lead Data Intelligence Project Manager
Cygnify · Dubai, Dubai, United Arab Emirates
Technology, Information and Internet · 11-50 employees
About the role
The Lead Data Intelligence Project Manager orchestrates data operations and acts as the primary liaison between external project teams and internal technical departments. They are responsible for managing the data lifecycle, overseeing labeling workforces, and ensuring data storage compliance and security.
What they look for
Requirements
Candidates must have 8+ years of project management experience, including at least 2 years in AI/ML data operations. A bachelor's degree in a relevant field is required, along with strong knowledge of machine learning lifecycles and project management tools.
Full description
About the role
As the Lead Data Intelligence Project Manager, you ensure that every project has the resources it needs to take off, you'll orchestrate data operations that power product development across the company's global teams. Key responsibilities include:
- Act as the primary liaison between our external project teams and our internal technical departments (Data Curation and MLOps). You will own the "data logistics" for the entire project lifecycle—from initial storage setup to final delivery of high-quality training sets.
- You will be the strategic lead for our labelling workforce, managing the balance between our in-house contractors and 3rd party service providers to ensure we never hit a capacity bottleneck.
- Overseeing data storage setup and maintenance to guarantee security, accessibility, and compliance.
- Maintaining rigorous documentation and workflows that ensure project transparency and repeatability.
- Supporting cross-functional communication between data teams, contractors, and engineering partners.
About you
- 8+ years in Project or Program Management, with at least 2 years specifically in AI/ML Data Operations or Data Management. You’ve delivered complex projects on time and within scope—often in fast-changing settings.
- Demonstrated success in managing 3rd party vendors or service providers, including contract negotiation, quality assurance, issue resolution, and ongoing relationship management.
- Machine Learning Lifecycle Knowledge: Strong understanding of how data flows from collection → cleaning → labeling → training → deployment.
- Tooling Familiarity: Experience with labeling platforms (Labelbox, CVAT, Label Studio) and project management tools (Jira, Asana, or Airtable).
- Data Literacy: Basic understanding of data storage architectures (S3, SQL, Snowflake) and data privacy regulations (GDPR, CCPA).
- Communication: Exceptional "translation" skills—the ability to explain technical MLOps constraints to business stakeholders and vice versa.
- A solid understanding of modern data storage solutions (cloud, on-premises, hybrid), including principles of data security, privacy, access control, and regulatory compliance.
- Able to translate complex technical requirements into clear actions for both technical and non-technical audiences. You’re adept at influencing and building consensus.
- Confident interfacing with cross-functional teams—coordinating with data scientists, engineers, product managers, and external stakeholders to deliver smooth handovers and alignment.
- Well-versed in project management tools and methodologies (e.g., Agile, Scrum, or Waterfall). Known for driving continuous improvement, clear documentation, and repeatable processes.
- Thorough, highly organized, and relentless in troubleshooting issues and removing blockers before they impact delivery.
- Proactive, accountable, and comfortable juggling competing priorities. You always keep end goals in focus and balance urgency with quality.
- Bachelor’s degree in a relevant discipline (e.g., Computer Science, Data Engineering, Engineering, or Information Technology); higher degree or PMP/Prince2 certification is a plus.
