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Data Science plays a key role in transforming vast amounts of structured and unstructured data into insights, building predictive analytics and machine learning (ML) models for complex business problems that enable the organization in data-driven decision making.

This is why we have recently created an analytics chapter within our Data and Digital Organization, to take advantage of analytics across our digital products.
Within the analytics chapter, you will work as an innovator, inventor, investigator, partner, engineer, ?€?firefighter'' and, of course, as a scientist.

You will be hands-on with implementation data and analytics pipelines, and build new business solutions and challenge the status quo.

In addition, you will be key to actively build new Data Products published in our Data Mesh and work closely with Subject matter experts within the manufacturing, quality and development organizations of PT to deliver fit-for-purpose-analytics.

Job responsibilities:
• Be part of a cross-functional use case team to address high-pressing business problems and become familiar with our product-related data, manufacturing processes and systems
• Undertake appropriate preprocessing of structured and unstructured data with additional support of Data Engineers and evolve and build new Data Products
• Analyze small to large amounts of static and real-time data to discover trends and patterns, and to implement analysis pipelines
• Build, validate, and deploy predictive models and Machine-Learning (ML) algorithms to predict manufacturing related issues of the products
• Maintain ML model lifecycle to ensure it fits for the intended use over time.

Job requirements:
Education and experience:
• Master's with 2 years relevant work experience, or PhD degree in Software Engineering, Statistics, Data Science, Bioinformatics or a related field.

Skills:
• Advanced knowledge of programming languages (Python, R) and ability to implement pipelines
• Proficiency in developing ETL data pipelines and using cloud computing to run pipelines and ML models (GCP/AWS)
• Extensive knowledge of ML frameworks, data structures, modeling, and software architecture.
• Rich experience in ML model validation and lifecycle management.
• In-depth knowledge of applying unsupervised and supervised models and ability to identify linear and non-linear relationships in data.
• Superb analytical and problem-solving abilities.
• Ability to communicate a compelling story and complex concepts with aspirational language that data professionals, business stakeholders and decision makers equally understand.