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Job Description:

募集部門の紹介:
我々データサイエンスグループは製造部門に所属し、国内にある3つの工場の仲間たちや間接部門、グローバル組織と協力しながら、製造収量の改善や生産性の向上などを目的とした高度なデータ分析やデジタルツインモデルの開発に取り組んでいます。

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.

職務内容:
技術移管の加速や医薬品製造プロセスの高度な自動制御化を目的として、Digital twin modelsの開発・実装をリードする
開発するモデル(予測・シミュレーション、ソフトセンシング)には、熱力学、輸送現象、流体力学、分子動力学などの第一原理に基づいたMechanistic model、ビッグデータに基づく統計/機械学習モデル、そしてそれらのHybrid modelが含まれる
GMP環境へのモデル実装に必要なドキュメントを準備し、製造部門や品質部門と協力しながら、モデルの実運用に向けての検証を成功させる
既存、もしくはこれから導入されるデジタルインフラやプラットフォームを最大限に利用して、実生産現場へのモデル実装を社内外のステークホルダーと共に推進し、サステイナブルなモデル利用方法の確立とモデルのライフサイクル管理を担当する
専門家としての知識を利用して、プロセス可視化、問題解決、予測モデリングなどに必要なデータの準備をサポートする
シミュレーションモデルや高度なデータ分析技術を利用して、高い製品品質を維持しながら生産収量や生産性の向上、逸脱や廃棄率・故障率の削減を実現する
関係者と協力してデータサイエンス技術の適用機会を特定し、多くの価値創造に貢献する

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.

応募要件:
<学歴>

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

<実務経験>

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

<スキル・資格>

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

<語学>

Fluent oral and written communication skills in Japanese and English

<その他>

Work in Office 8 days/month or more
Business Trips Depending on the assigned project, domestic manufacturing site visits may occur


求める人物像:
データサイエンスや数値シミュレーションモデリングの専門家として適切なソリューションを提案し、社内外のステークホルダーと連携しながらデジタルツインモデルの開発・実装を強力にドライブできる方
最新技術をサスティナブルな形で現場実装し、製品品質を高いレベルで維持しながら生産収率や生産性の向上を実現することに歓びを感じることができる方
国や組織、考え方など多様性ある環境を好み、国内外を問わず仲間たちと協力しながらプロジェクトを推進する状況を楽しめる方

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 , Production Sites (Osaka/ Yamaguchi , (Narita , Research Site (Kanagawa
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), JapanHikari, Japan, Tokyo, Japan


Worker Type:
Employee


Worker Sub-Type:
Regular


Time Type:
Full time
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