Head of Data Onboarding & Dataset Operations

Location:
London / US Hybrid / Remote
Employment Type:
Full-time
Department:
Data Operations

About Scalpel

Medical errors are the third leading cause of death, with many occurring during surgery. Scalpel is on a mission to solve this problem by leveraging cutting-edge computer vision and AI-driven solutions to improve surgical safety.

We are a fast-growing health-tech startup with a dynamic and highly skilled team. Our engineering culture values collaboration, innovation, and problem-solving. As part of our team, you’ll work on real-world challenges that directly impact patient safety worldwide.

About the Role

We are looking for aHead of Data Onboarding & Dataset Operations, a cross-functional leader who will design, run, and continuously improve the entire end-to-end process of dataset creation across vendors, hospitals, and product lines.

This person is accountable for the strategy, execution, quality, and timeliness of onboarding thousands of trays into the Scalpel AI platform.

You will be the operational brain behind the dataset — the human equivalent of the AI “Virtual Manager” — ensuring every tray moves predictably from nomination → capture → annotation → model training → deployment → monitoring.

This role combines operational leadership, technical systems thinking, and cross-team orchestration.

Your Responsibilities

1. Own the Global Dataset Strategy
  • Define the roadmap for onboarding vendor datasets (Stryker, Zimmer, J&J, S&N, others).
  • Translate customer pipeline into a scalable onboarding plan.
  • Prioritise trays and vendors that unlock the most commercial value.
  • Drive quarterly coverage targets across specialties (Joint Reconstruction, Spine, Trauma, General Surgery).
2. Lead the Tray Lifecycle End to End

Be accountable for the entire lifecycle: Nomination → BOM validation → Capture → QC → Annotation → Training → Model QA → Deployment → Monitoring

  • Ensure each tray progresses through the lifecycle within defined SLAs.
  • Flag delays, anticipate risks, and escalate appropriately.
  • Maintain complete operational transparency for internal teams.
3. Build & Run the Data Capture and Annotation Engine
  • Manage capture squads (OEM capture, warehouse capture, hospital capture).
  • Scale annotation workflows internally and via external vendors.
  • Establish image quality, annotation accuracy, and variant identification standards.
  • Ensure predictable throughput for 50–100 trays per month.
4. Develop Core Operational Infrastructure
  • Define and maintain theTrayPassport data model (lifecycle, timestamps, SLAs, owners).
  • Partner with Platform Engineering to build dashboards for:
    • Tray status tracking
    • Backlog and risk prediction
    • Accuracy monitoring
    • Vendor coverage
  • Define and enforce lineage, auditability, and version control.
5. Manage BOM Variation, Variant Drift & Modular Trays
  • Oversee resolution of discrepancies between OEM BOMs, distributor BOMs, SPD configurations, and surgeon preferences.
  • Ensure correct mapping of variants, sizes, and similar-looking instruments.
  • Work with Product and CV to build processes to handle missing, extra, migrated, or unknown instruments in real time.
6. Drive Cross-Functional Alignment

Work closely with:

Computer Vision

  • Plan training cycles, retraining schedules, and model QA.
  • Provide them with high-quality, structured data and active feedback loops.

Data Engineering

  • Streamline ingestion pipelines, automate QC, and ensure data integrity.

Ops & Customer Success

  • Plan data capture at OEMs, warehouses, and hospitals.
  • Ensure readiness for demos, pilots, and go-lives.

Product

  • Improve internal tools (Capture app, annotation workflows, dashboards).
7. Forecasting & Planning
  • Create monthly/quarterly forecasts for:
    • Capture capacity
    • Annotation hours
    • Training compute requirements
    • Squad staffing
  • Anticipate bottlenecks weeks in advance.
8. Implement Continuous Improvement Culture
  • Continuously optimise capture protocols, SOPs, and workflows.
  • Integrate Rapid Capture Protocols (RCP).
  • Incorporate synthetic data and CAD-to-Blender pipelines where beneficial.
  • Reduce end-to-end tray onboarding timelines from weeks → days.
9. Metrics & Reporting
  • Maintain target KPIs:
    • ≥95% first-pass accuracy on new models
    • ≥99% accuracy after retraining
    • SLA adherence (capture, annotation, training)
    • Weekly throughput per squad
    • Dataset readiness for each vendor
  • Publish weekly dataset operations reports to leadership.
10. Risk Management
  • Identify operational risks early:
    • Capture delays
    • Annotation backlogs
    • BOM inconsistencies
    • Accuracy failures
    • Drift
  • Implement standard responses, escalation paths, and corrective actions.
What Success Looks Like in 12 Months
  • 1,000+trays onboarded across major vendors.
  • End-to-end onboarding time reduced to <7 days per tray.
  • Predictable, SLA-driven pipeline with zero surprises.
  • High accuracy models deployed for all priority vendors.
  • Dataset operations function running like a machine.
  • CV team receiving clean, consistent, high-quality training data.

Who We're Looking For

Technical / Operational
  • 7+ years’ experience in:
    • Data operations, ML operations, computer vision ops, or scaled technical workflows.
    • Running large-scale annotation or data pipelines.
    • Managing execution-heavy operations with SLAs.
  • Strong familiarity with CV/ML workflows (not deep research, but operational).
  • Experience managing vendor teams, offshore teams, or large-scale annotation vendors.
Leadership
  • Proven cross-functional leadership across engineering, product, and operations.
  • Head of Data Onboarding & Dataset Operations 5
  • Experience managing teams and external vendors.
  • Strong ownership mindset and ability to run mission-critical programs.
Soft Skills
  • Highly structured thinker.
  • Excellent communication skills.
  • Strong prioritisation ability.
  • Comfortable switching between high-level strategy and tactical execution.
Nice to Have
  • Experience in surgical workflows, medical devices, healthcare supply chain, or robotics.
  • Exposure to synthetic data generation (CAD, Blender).
  • Experience building internal tools for dataset management.

Why Join Us?

Impact That Matters – Your work directly influences the safety of surgical procedures worldwide

Work on Cutting-Edge AI – Be part of a team working with state-of-the-art computer vision and ML pipelines

Competitive Salary & Benefits – Including a pension scheme and 20 days of holiday + bank holidays

Start-up Energy – Join a fast-moving, mission-driven team with big ambitions and strong backing

How to Apply

Send your CV and a brief cover letter to jobs@scalpel.ai