Data Analyst

We are seeking a technically strong Data Analyst to work at the intersection of engineering, device testing, and data analytics within our Healthcare Biosensors group. 
 

This is a hands‑on analytics role, focused on implementing robust data pipelines, reproducible analysis methodologies, and engineering‑facing dashboards that help teams evaluate device performance, reliability, yield, and quality throughout development and manufacturing. 
 

This role is not an algorithm research position. It is intended for analysts with a strong technical foundation (SQL, Python, statistics) who enjoy working closely with engineers on real production, test, and validation data. 
 

Strong candidates often come from adjacent engineering‑led domains such as medical devices, automotive, industrial manufacturing, or other regulated or high‑rigour product environments. Direct biosensor experience is beneficial but not required. 
 

Roles & Responsibilities 

In this role you will: 

• Play a key role in enabling engineers and scientists to make confident, data‑driven decisions on complex devices by ensuring data is trustworthy, analysable, and consistently interpreted across teams. 

• Design and maintain standardised, repeatable data analysis workflows used by device, test, and reliability engineers. 

• Build SQL and Python‑based analysis pipelines for processing, device characterisation data, test & verification datasets, reliability and yield metrics. 

• Develop engineering‑focused dashboards (Power BI / similar) that visualize trends, pass/fail behaviour, and performance metrics. 

• Work with engineering and manufacturing teams to define metrics and KPIs. 

• Ensure data traceability, versioning, and reproducibility across datasets. 

• Apply statistical methods (trend analysis, variance analysis, cohort analysis, anomaly detection) to support engineering decision‑making. 

• Support analysis of production and yield data, identifying trends, root causes, and improvement opportunities. 

• Document data models, assumptions, and analysis outputs in a way that supports review, audit, and reuse. 

Qualifications & Requirements 

Required: 

• Bachelor’s or Master’s degree in Engineering, Physics, Computer Science, Data Analytics, or a closely related technical discipline. 

• 4-8 years of hands‑on data analytics experience in engineering, manufacturing, industrial, scientific, or regulated environments. 

• Strong practical experience with Python for data analysis. 

• Experience working with real‑world engineering or production data, such as test or validation datasets, reliability or quality data and manufacturing / yield metrics. 

• Proven ability to turn messy, ambiguous datasets into structured, repeatable analysis. 

• Experience building dashboards for technical stakeholders. (Power BI or similar) 

• Comfortable collaborating with engineers and explaining data insights clearly. 

• Strong communication, documentation, and collaboration skills. 

• Ability to work independently and as part of a multidisciplinary team. 
   

Desired: 

• Exposure to medical devices, sensors, automotive, or other regulated products. 

• Familiarity with data traceability, validation, or quality systems. 

• Experience integrating data from multiple sources (e.g. test systems, manufacturing systems) 

• Experience applying statistical or ML‑based techniques (e.g. anomaly detection, trend analysis) to engineering analytics (application‑focused; not research or algorithm‑development roles) 
   

Role Fit Clarification 

This role is well‑suited for candidates who: 

• Are hands‑on with SQL and Python and enjoy owning analysis end‑to‑end. 

• Work comfortably with engineering, test, or manufacturing teams. 

• Care about reproducibility, traceability, and disciplined analysis methods. 

• Enjoy building analysis pipelines and dashboards that engineers rely on. 

This role is not intended for candidates whose primary focus is: 

• Algorithm or ML research 

• Academic or PhD‑focused modelling 

• BI‑only or reporting‑only roles without hands‑on data processing