Data Scientist ― Advanced Process Modeling Expert Full-time

Data Science Group Position

Our Data Science group is part of the manufacturing division, working in collaboration with colleagues from three manufacturing sites in Japan, indirect departments, and global organizations to conduct advanced data analysis and develop digital twin models aimed at improving manufacturing yield and enhancing productivity.

Lead the development and implementation of digital twin models aimed at accelerating technology transfer and advancing the automation of pharmaceutical manufacturing processes. The models to be developed (predictive, simulation, soft sensing) include mechanistic models based on first principles such as thermodynamics, transport phenomena, fluid dynamics, and molecular dynamics, as well as statistical/machine learning models derived from big data and hybrid models combining both.

Prepare the necessary documentation for implementing the models in a GMP environment, collaborating with manufacturing and quality departments to successfully validate the models for operational use.

Leverage existing or future digital infrastructures and platforms to facilitate model implementation in real production environments alongside internal and external stakeholders, while establishing a sustainable model utilization approach and managing the model lifecycle.

Support data preparation required for process visualization, problem-solving, and predictive modeling by utilizing expertise as a data scientist.

Utilize simulation models and advanced data analytics to improve production yield and productivity, while reducing deviations and discard/failure rates, all while maintaining high product quality.

Collaborate with stakeholders to identify opportunities for applying data science technologies and contribute to creating significant value.

University degree in STEM (Science, Technology, Engineering or Mathematics – preferably: Chemical/Biochemical Engineering) with a post-graduate degree (Masters/PhD) would be highly desirable.

3+ years in pharmaceutical/chemical/biotech industry.

Hands-on experience with digitizing industrial processes, computational modeling, process simulation, soft sensor modeling (PAT) and data analytics.

Excellent knowledgeable with statistical/machine learning/deep learning/AI methodologies.

Familiar with cGMP requirements and quality system.

Hardware experience (e.g. building experimental setups).

Capabilities to translate business needs into data analytics concepts and the other way.

Demonstrated ability to develop innovative solutions for real-world business problems.

High level project management skills.

Ability to interface with international stakeholders and to connect internal and external data analytics experts of both academia and industries.

Strong expertise in Machine Learning and AI/Deep Learning algorithms (PCA, PLS, RF, XGB, SVM, LSTM, etc.), cross-validation and hyper-parameter tuning techniques, model interpretation and deployment.

Expertise in mechanistic and hybrid modeling (e.g. gPROMS, Aspen+).

Expertise in (multi-variate and multi-step) Time Series Analysis.

Experience with Soft sensor development for drug manufacturing processes is a strong asset.

Good programming knowledge of Python required, further skills such as GitHub, Julia, SQL, PowerBI, Plotly, Streamlit, R, RShiny, etc. are advantageous.

Experience with Databricks, SIMCA Online & Offline, Dataiku, DataRobot, AspenTech Inmation, OSI PI, Discoverant.

Prior experience of SCRUM and other project management methodologies is a strong asset.

Fluent oral and written communication skills in Japanese and English.

Work in Office: 8 days/month or more.

Business Trips: Depending on the assigned project, domestic manufacturing site visits may occur.

Expertise in data science and numerical simulation modeling, with the ability to propose suitable solutions and drive the development and implementation of digital twin models in collaboration with internal and external stakeholders.

Passion for sustainably implementing cutting-edge technologies on-site, achieving improvements in production yield and productivity while maintaining high levels of product quality.

Enjoys working in a diverse environment with varying perspectives, countries, and organizations, and thrives on advancing projects in collaboration with colleagues both domestically and internationally.

Takeda Compensation and Benefits Summary:

Allowances: Commutation, Housing, Overtime Work etc.

Salary Increase: Annually, Bonus Payment: Twice a year

Working Hours: Headquarters (Osaka/ Tokyo) 9:00-17:30, Production Sites (Osaka/ Yamaguchi) 8:00-16:45, (Narita) 8:30-17:15, Research Site (Kanagawa) 9:00-17:45

Holidays: Saturdays, Sundays, National Holidays, May Day, Year-End Holidays etc. (approx. 123 days in a year)

Paid Leaves: Annual Paid Leave, Special Paid Leave, Sick Leave, Family Support Leave, Maternity Leave, Childcare Leave, Family Nursing Leave.

Flexible Work Styles: Flextime, Telework

Benefits: Social Insurance, Retirement and Corporate Pension, Employee Stock Ownership Program, etc.

Important Notice concerning working conditions:

It is possible the job scope may change at the company's discretion.

It is possible the department and workplace may change at the company's discretion.

Locations:

Osaka (Juso), Japan

Hikari, Japan

Tokyo, Japan

Worker Type: Employee

Worker Sub-Type: Regular

Time Type: Full time