Data Scientist

• Lead PO data cleansing and validation activities across MSD, including closing redundant or inactive POs and correcting data anomalies
• Assess migration readiness by validating Period of Performance (POP) data and determining which POs and PO lines will transition to Costpoint
• Support data mapping and preparation activities to enable a successful ERP migration
• Define and implement standardised PO structures and develop templates to support consistent procurement practices
• Partner with Operational Units to align existing POs with agreed standards and business rules
• Support the implementation of cyber security questionnaire requirements within procurement processes
• Coordinate cross-functional stakeholder engagement to validate procurement data and support broader cleansing initiatives
• Assist with procurement-related security role mapping and process alignment between MSD and Costpoint
• Provide guidance and training to business users on Costpoint P2P processes and improved procurement practices

Procurement and P2P Data Lead

• Help the Maintenance team better utilise existing data sources.
• Train deployable predictive maintenance models which improve the reliability and lifecycle performance of PhotonAssay™ automation, detector and linear accelerator systems.
• Analyse maintenance and fault data to identify recurring failures and reliability risks.
• Perform data analytics and visualisations to provide actionable insights.
• Develop and refine condition monitoring and preventive maintenance strategies for PhotonAssay™ automation, detector and accelerator systems.
• Train reliable and deployable Machine Learning models for predictive maintenance.
• Optimise maintenance tasks and frequencies using data rather than fixed schedules, and incorporate approved strategies into MaintainX (maintenance management system) configuration, workflows, and maintenance programs.
• Contribute to better preventive maintenance practices, resulting in improved PhotonAssay™ availability (less breakdowns), without increasing maintenance costs.
• Recommend any additional sensors or measurements required if existing data sources are inadequate.
• Support the accurate, complete, and consistent capture of fault, maintenance, and spares consumption data within Chrysos systems.
• Analyse data and prepare structured insights and business impact assessments to inform and support prioritisation of improvement initiatives, including downtime and cost considerations.
• Use Automation, Detector and Linac records and data streams for trend analysis, and develop dashboards.
• Support spare parts optimisation by linking failure rates to criticality, stocking levels, and lifecycle forecasts.
• Assist with maintenance planning improvements to balance reliability, cost, and operational availability.
• Promote and support a strong workplace health and safety culture.
• Perform other duties as reasonably required to support maintenance and operational excellence.
• Support global collaboration across regions where reliability insights can deliver value.

• Build predictive defences: Use data and models to support the development of risk mitigation strategies and interventions while preserving and improving the user experience, building, training, and iterating on predictive models that stand up to real adversaries at Kasada scale.
• Evaluate rigorously: Pressure test your own work before anyone else has to. Evaluate model performance and trade-offs carefully, so detection decisions stand up to scrutiny from engineering, product, and security operations long after the model goes live.
• Partner with engineering, research, and product: Work alongside engineers, researchers, and product managers to take models from notebook to production, integrating them into Kasada's platform and iterating on them as customer needs and attacker behaviour shift.
• Make models legible: Make your models and their outputs understandable to both technical and non-technical colleagues. Detection decisions should never be a black box to the teams that rely on them, and that bar sits with you.
• Hunt adversarial patterns: Analyse large datasets to surface anomalies, identify adversarial behaviours, and flag emerging attack patterns. Stay current with developments in adversarial ML and cybersecurity, and apply relevant techniques to strengthen our defence capabilities.

Data Scientist – Fraud

• Own and shape end-to-end data strategy for China, Japan, South Korea, and India
• Serve as senior analytics partner to CJKI country leaders and regional marketing leads
• Ensure voice of CJKI users is embedded in Canva’s global product and growth strategy
• Uncover structural drivers of growth and friction
• Define best-in-class experimentation and measurement standards
• Oversee evolution of trusted, scalable data products
• Act as a force multiplier within the International Data team

Staff Data Scientist – International, CJKI

• Delivering data-driven insights, experimentation, and measurement initiatives across marketing and business functions.
• Work closely with cross-functional teams, including analytics, data engineering, and business stakeholders, within a large-scale enterprise environment.

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Procurement and P2P Data Lead

Data Scientist

Data Scientist – Fraud

Staff Data Scientist – International, CJKI

Data Scientist