Process Optimization Specialist

Vor 5 Tagen

Neuenburg, Neuenburg, Schweiz Takeda Vollzeit

About the Role

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We are seeking an experienced Predictive Modeling Scientist to join our team in Neuchâtel, Switzerland. As a key member of our data science team, you will play a critical role in driving business success through data-driven insights.

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Main Responsibilities

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  • Develop predictive models with SMEs through adequate tools like Simca to improve process robustness and efficiency.\
  • Own, manage, execute, and maintain local data science initiatives.\
  • Gather and identify local data science possibilities/initiatives/requests in collaboration with local teams, IT, Engineering, Quality, and global GMS data science.\
  • Provide the local link between local and global GMS data science, local functions, Quality, IT, Engineering in the respective region/Operational Business Unit.\
  • Drive the local execution and implementation of GMS Data Science strategy and initiatives.
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Key Qualifications

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  • University degree in science/business.\
  • Advanced degree (Master/MBA and/or PhD) in a highly quantitative field, e.g., Engineering, Mathematics, IT, Economics, or Physics with a focus on Control Theory/Statistics/Mechatronics, Information/Operation, Management/Strategy/Economics, Technology, and Innovation Management.\
  • 2+ years of work experience in a data analysis-related position or equivalent academic experience/course record.\
  • Hands-on experience in applying and executing Artificial Intelligence/Deep Learning, Machine Learning, Big Data, and Digital with respective programming knowledge.\
  • Language proficiency in French (B2) or willingness to learn and English (C1).
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Required Skills

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  • Strong expertise and experience in Artificial Intelligence/Deep Learning, Machine Learning, Big Data, and Digital.\
  • Proficient in programming languages such as Python/R/C; further programming experience is advantageous.\
  • Proficiency in database languages (SQL) and understanding of data models and databases.\
  • Experience in compositional data analysis and analyzing time series data.\
  • Knowledge of distributed computing, stochastic modeling, sampling methods, and randomized algorithms.\
  • Familiarity with explainable Artificial Intelligence methods.

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