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Manager Data Analytics Tokyo


05-31 Manager Data Analytics/データアナリティクスマネージャー


Life Sciences

11 million
1.Gathers and identifies local data analytics. possibilities/initiatives/requests in collaboration with IT and global GMSGQ data analytics.
2.Drives the execution for local implementation of GMSGQ data analytics roadmap initiatives.
3.Owns, manages and drives local data analytics initiatives, such as for Predictive Maintenance, Down Time Analysis and respective Database management. Manages the respective functional and cross-functional inter dependencies accordingly.
4.Manages and drives local process monitoring and process improvement in collaboration with global GMSGQ data analytics (e.g. descriptive statistics and visualization, statistical process control, out-of-the-box-tools).
5.Manages, drives and executes respective reporting in collaboration with global GMSGQ data analytics (e.g. automated daily/monthly/annual, Stability Analysis Targets).
6.Supports and participates in global/functional/local GMSGQ data analytics community.
1.Expertise and experience in Digital/Big Data Analytics/ Artificial Intelligence/Machine Learning:
2.Expertise in Signal Processing, Control Systems, Mechatronics
3.Knowledge of Statistical Process Control.
4.Experience in Machine Learning algorithms (CNN, SVM, LSTM, etc.).
5.Respective programming knowledge of either MATLAB/Python/R required, further skills such as C++, SQL etc. are advantageous.
6.Experience in quality mapping of parameters is a plus.
7.Expertise in Bayesian statistics.
8.Experience in Density estimation.
9.Knowledge in Bayesian Hypothesis Testing.
10.Knowledge in ANOVA and forecasting techniques (ARIMA, ARMA, NARX).
11.Prior experience of Biostatistics, stochastic modelling and decision trees is a strong asset.
12.University degree of science/business. BSc degree is required, e.g. in Engineering , Mathematics, IT or Physics background with focus on e.g. Control Theory/ Statistics/ Mechatronics, Information/Operation, Technology, Innovation Management.
13.Capabilities to translate business needs into data analytics concepts and the other way.
14.Hands-on, able to implement and execute initiatives
15.High level project management skills.
16.Ability to interface with international stakeholders and to connect internal and external data analytics experts of both academia and industries.
17.Fluent written and spoken English mandatory.
Travel requirements
Willingness to travel to various internal/external meeting.