Bolton, Greater Manchester
£45.00 - £50.00 per hour
about 1 month ago
Quality Information Systems Data Science Developer
Location: Bolton, Lancashire
Duration: 6 Months
Rate: £50.00 per hour
As a Developer working within the Product Assurance Systems & Tools Team, you will be responsible for managing a series of task packages specifically around our clients Product Quality and Non-conformance Management tools and process.
Ensuring a successful roll out and early life support of the Google Cloud Platform (GCP) Natural Language Processing (NLP) platform, management of the cultural change and ensuring the tool is correctly utilised for verification and identification of patterns and trends within the dataset. Tailoring the tool and visualisations to suit the specific needs of the business user base during pilot phase.
Documentation of the SAP S4 Quality requirements based on use case exploration. This shall include demonstration of tool functionality in a sandpit environment to ensure the business expectations are appropriate and managed.
The evaluation and use case mapping of a Quality Predictive Model, leading to a proof of concept activity based on the findings and business aspiration. This model shall utilise multiple company data sets and the application of Machine Learning and Artificial inelegance techniques to predict product quality events allowing preventative action implementation.
Implementation of data labelling techniques to allow the best value to be delivered from historic and legacy datasets.
EXPERIENCE / KNOWLEDGE / QUALITIES:
Relevant experience in supporting Quality or Information System activities within the Aerospace environment. Extensive knowledge of the Google Cloud Architecture and associated packages.
Experience with data visualisation tools, such as Data Studio, Power Bi, SAP BW, Sql Server Reporting Services, etc.
Proficiency in using query languages such as SQL, ABAP.
Experience with Oracle NoSQL databases, such as MongoDB, Redis and Google BigQuery.
Good knowledge of information security requirements surrounding official/sensitive datasets
Ability of selecting features, building and optimizing classifiers using machine-learning techniques.
Extending company data with third party sources of information.
Enhancing data collection procedures to include information that is relevant for building analytic systems and improving existing datasets.
Processing, cleansing, and verifying the integrity of data used for analysis.
Doing ad-hoc analysis and presenting results in a clear manner.
Creating automated anomaly detection systems and classifying data quality.
Excellent understanding of machine learning techniques and algorithms, such as TFI DF Functions, Naive Bayes, Decision Forests, Neural Networks etc.
Experience with common data science toolkits, such as R, Python, Tensor flow, NumPy and MatLab, etc.
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