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Director, Data Scientist Tokyo

TBG-019810
Position

11-27 Director, Data Scientist /データサイエンティストディレクター

Company

Life Sciences

Salary(¥):
16 million
Responsibilities
 1. Drives the design, execution and implementation of advanced analytics for GMSGQ (e.g. Continuous Process Improvement, end-to-end Simulation, advanced statistics, modeling simulation)
2. Develops and owns detailed scale-up and delivery execution roadmap for data analytics within GMSGQ and manages respective functional and cross-functional interdependencies
3. Owns, manages and executes process monitoring and process improvement in collaboration with local and functional GMSGQ data analytics (e.g. descriptive statistics and visualization, statistical process control, out-of-the-box-tools).
4. Owns, manages and executes respective Reporting in collaboration with local GMSGQ and functional data analytics (e.g. automated daily/monthly/annual, Stability Analysis Targets)
5. Gathers, identifies and reviews GMSGQ data analytics possibilities/initiatives/requests in collaboration with IT and GMSGQ business stakeholders. Advises on the respective short and medium focus.
6. Drives and executes on digital GMSGQ Strategy, Technology and Innovation (e.g. Productivity Analysis, Key Performance Indicator tracking, Interdependencies, Evolution of Internet of Things)
7. Builds up and owns global/functional/local GMSGQ data analytics community.

労働時間・賃金の詳細、試用期間の有無、加入保険等の詳細は求職者と面接の際に明示予定

Requirements
1. Expertise and experience in Digital/Big Data Analytics/ Artificial intelligence/Machine Learning:
2. Expertise in Signal Processing, Control Systems, Mechatronics
3. Strong 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. Solid knowledge in Bayesian Hypothesis Testing,
10. Solid 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. Advanced degree is required (Master/MBA and/or PhD), e.g. in Engineering , Mathematics, IT or Physics with focus on either Control Theory/Statistics/Mechatronics, Information/Operation, Management/Strategy, Technology and Innovation Management