Data Partnerships Project Manager

About us

Qureight’s mission is to accelerate clinical trials and ensure breakthroughs in lung and heart disease reach patients without delay. Our AI-powered data and imaging curation platform enables the analysis of clinical imaging and other healthcare data, helping our customers bring treatments to market, faster.

We’re looking for talented people who want their work to matter. With offices in Cambridge and London, you’ll join our multidisciplinary team of clinicians, scientists, and engineers. What unites us is our open culture, continuous learning mindset, and a shared mission to help biopharma run faster, smarter trials.

We partner with leading pharmaceutical and biotech companies, as well as hospitals and research institutions, to unlock the value of clinical data and deliver AI-driven insights in clinical trials. We are currently scaling our data partnerships and platform capabilities to support a growing portfolio of AI models and commercial programmes.

We are looking for a Data Partnerships Project Manager to lead the management of critical data partnerships. This role sits at the intersection of:

• Technical project delivery
• Partnerships (hospitals, universities, biopharma)
• Data sourcing and acquisition
• Legal and governance

You will manage complex, high-value projects with external stakeholders. This will range from highly structured, contractually defined biopharma companies to smaller, often less well-defined data collaborations with universities and research groups. Across both, you will bring the right people into the room, set and hold the team to clear timelines, and ensure deliverables are agreed and understood on both sides. You will also play a key role in ensuring that Qureight has access to high-quality, well-structured data to train, validate, and deploy our AI models.

This is a high-impact, cross-functional role requiring strong relationship management, operational rigour, and comfort working across technical domains.

1. Project manage data partnerships (end-to-end)

• Own delivery of data partnerships from initial engagement and contracting to data transfer, ingestion and ongoing management / delivery
• Coordinate across external partners and internal machine learning, data engineering, legal, commercial and product teams
• Work with legal and commercial teams to ensure Qureight puts appropriate data sharing and licensing agreements in place
• Ensure delivery of data assets and statistical outputs aligned with:
• Commercial agreements
• Project timelines
• Track timelines, dependencies, and risks

2. Enable data ingestion and readiness

• Work closely with technical teams to:
• Take guidance on what represents ‘good data’
• Ensure data is delivered in usable formats
• Resolve data quality and completeness issues
• Support data standardisation and annotation workflows
• Act as the bridge between:
• External data providers
• Internal data/ML teams

3. Support data sourcing and acquisition

• Support the CSO and other senior leaders to identify and secure access to datasets from:
• Hospitals and healthcare providers
• Universities and research institutions
• Commercial data partners
• Build and manage relationships with key stakeholders, including:
• Clinicians
• Researchers
• Technology transfer offices (TTOs)

4. Build scalable partnership processes

• Develop repeatable processes for:
• Contracting
• Partner onboarding
• Data ingestion
• Project delivery
• Help scale Qureight’s data partnerships function as the company grows

• Proven experience managing complex, data-rich projects
• Proven experience working with hospitals / healthcare providers and universities / research organisations
• Strong track record of building and maintaining relationships with external partners and cross-functional internal stakeholders
• Comfortable collaborating with legal teams and working with data sharing and licensing agreements
• Experience in sourcing, negotiating, or managing access to datasets

• Understanding of data ingestion workflows and data formats and quality challenges

• Ability to work effectively and cross-functionally with machine learning, data science, data engineering, legal, commercial and product teams

• Familiarity with GxP / regulated software…