Tech Ops Data Analyst (Programmer)

Part of the Technical Operations team's work is to monitor the performance of the flow cell manufacturing process in response to changes in the procedures, input materials or unforeseen events. This requires analysing data to identify associations between performance and its manufacture that inform decisions regarding what actions to take to improve performance. Much of this is achieved through the analysis of telemetry – data generated by testing and use of products – alongside data documenting how it has been manufactured. However, there are aspects of the manufacturing process that do not always represent processes sufficiently well to be able to reliably identify likely causes of performance issues. This exciting and challenging role is responsible for extending the coverage of manufacturing data and analysing it to establish its explanatory power: to understand what aspects, if any, of manufacturing performance are associated with the data. To varying degrees, the role encompasses the entire data pipeline, including developing Extract‑Transform‑Load processes through to analysing data to establish associative and causal relationships.

Data representing the quality of individual processes and input materials is a key component of the Predictive Manufacturing initiative, which includes assessment and development of Causal Inference statistical techniques. Applicants with experience or interest in this area would have an opportunity to develop further in this discipline as it moves through proof‑of‑concept and applied stages of development. The role will strengthen Technical Operations' analysis capability to provide more comprehensive data coverage, develop robust analysis procedures, and create efficient pipelines that connect source databases to actionable dashboards.

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• Provide more comprehensive coverage of data representing individual stages of flow cell manufacture.
  
• Develop analysis procedures to assess, in the context of flow cell manufacture, the utility of existing and new data.
  
• Build and maintain Python tools that efficiently process, summarise, and classify large volumes of data, creating data pipelines that connect source databases to dashboards of results.
  
• Quickly establish a strong understanding of the science behind the product and convert data into actionable insights regarding product performance and failure types.
  
• Extend the data warehouse that underpins manufacturing operations, incorporating new data sources and improving data quality.
  

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• Strong Python programming skills and experience with Pandas, NumPy, and Matplotlib/Seaborn.
  
• Good understanding of statistical concepts and principles.
  
• Proven ability to interpret data and understand underlying definitions.
  
• Ability to assess the degree of confidence in findings, detect data quality issues, identify inconsistencies, and filter out noise.
  
• Excellent attention to detail, inquisitive nature, and ability to apply scientific rigor.
  
• Strong presentation skills and confident communication with multidisciplinary teams.
  
• Proficiency with MongoDB, MySQL, Spotfire or Tableau, and GitLab.
  
• Ideally holds a numerate degree in Mathematics, Statistics, Physics or Computer Science.
  
• Highly motivated, adaptable, and able to perform well under pressure.
  

We offer an attractive bonus, generous pension contributions, private healthcare, and an excellent starting salary. Oxford Nanopore Technologies is headquartered at the Oxford Science Park outside Oxford, UK, with global operations and a culture of innovation and ambition.