Data Scientist

In this role the successful candidate will work alongside a talented group of engineers and scientists supporting the industrialisation of Li-ion batteries and cells. Reporting to the Head of Product Engineering this position will support the capture and analysis of data generated from the production and test of UKBIC baseline and customer products.

The purpose of this role is to deliver data insights that assess the impact of manufacturing variation on product performance, thereby determining product and process improvement opportunities to enable successful scale-up to quality.

The company has recently deployed a Manufacturing Execution System (MES) to capture equipment, process and quality data across its facilities and is developing the associated data pipelines into Siemens Insights Hub. This role will act as the Product Engineering owner and internal customer for analytics capability development, working closely with the Data Engineer and Manufacturing Systems Integration Engineer to define data structures, traceability requirements, dashboards and analytical workflows.

The successful candidate will help shape the evolution of Insights Hub from foundational time-series data visualisation and reporting through to advanced statistical analysis, modelling, machine learning and predictive analytics capabilities.

The analytics capability is expected to evolve in stages, beginning with establishing access to manufacturing and test data through time-series visualisation and traceability, before advancing into variable-to-variable correlations, process characterisation, feature engineering, statistical modelling, predictive analytics and machine learning. The successful candidate will play a leading role in defining and delivering this capability roadmap.

In summary, the key deliverables for this role are:

1. Define and deliver the Product Engineering analytics roadmap for Siemens Insights Hub.

2. Deliver and report data insights to customer and internal programmes, on time and to target.

3. Develop and continuously improve analytical methods, models and tools that support data-driven decision making.

4. Capture, document and communicate analytical methods, models and findings to build organisational capability.

5. Develop code to support the above activity and implement processes that ensure these are stored securely and version controlled.

• Act as the Product Engineering owner and internal customer for Siemens Insights Hub analytics capability development.
• Define data, traceability, reporting and dashboard requirements in collaboration with Data Engineering and Manufacturing Systems Integration teams.
• Promote a culture of data-driven decision making through effective analysis, visualisation and communication of insights.
• Determine relationships between manufacturing process parameters, quality measurements, defects and product performance.
• Analyse manufacturing and test data to identify trends, root causes and opportunities for product, process, yield and quality improvement.
• Develop derived metrics, calculated features and engineering insights from raw manufacturing and test data.
• Apply statistical methods, data modelling and machine learning techniques to improve process understanding and predict product outcomes.
• Create and programme data models and analytical tools using simulated and real-world datasets to support engineering and business decisions.
• Provide analytical inputs to other engineering capabilities, including product and process simulation activities.
• Implement and execute good practice in the secure storage and control of developed code.
• Make data-driven recommendations that facilitate the continuous improvement of manufacturing processes and product performance

• As well as working directly with Product Engineering peers the Data scientists will work cross functionally within the project team including:
• Manufacturing Engineering & Quality – Capturing and post processing of on-line of off-line measurement data. Determine data dashboard requirement for Siemens Insights Hub.
• Digital Manufacturing – Working closely with the Data Engineer and…