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Job Description

Takeda is unwavering in our commitment to patients. With the patient at the center of everything we do, we continue to innovate and drive changes that will better their lives - we’re looking for like-minded professionals to join us.

Takeda is a global industry leader –we are united by our four priorities (Patients, Trust, Reputation, and Business) and our shared values of Takeda-ism: Integrity, Fairness, Honesty, and Perseverance. Takeda-ism is more than just a word. It’s a belief and a mindset which guides our decisions, behaviors, and interactions. It’s how we strive to work and live, every day.

About the Role

Support an In Silico First paradigm in Pharm Sci by building & supporting the deployment of industry-leading smart algorithms & predictive models (both statistical & first principle) to support optimization of control loops, platform performance & CMC workflows to advance pipeline programs.Support & implement lab & pilot-scale platforms, collect & analyzes structured & unstructured data of product & process and generates scientific reports including conclusions & recommendationsBe recognized as a technical resource & expert across PharmSci and the broader R&D community for mathematical modeling, harnessing next generation capabilities in the field of Data Science and utilization of his/ her technical expertise to contribute across multiple projects and drive technical & scientific strategyLead and undertake scientific research, complex troubleshooting & problem-solving activities; able to do so while applying Machine/Deep LearningKeep abreast of technological advances specific to Digital Acceleration, Data Science, Data Engineering, Computational Process Development, Systems Biology, and Advanced Process Control.Support the In Silico First initiative by representing the PSST team while interacting with different functions as a business and technical expert, providing scientific support to the respective functional area & communicating central guidance & support from the global suites to the local teams

ACCOUNTABILITIES

Support projects, primarily within a framework of the In-Silico First (ISF) initiative via the following:Review, interpret and communicate data cross-functionally across PharmSci and project teamsPerform or support the performance of Statistic/Machine/Deep Learning and other advanced data techniques in support of ISF PoCs towards deriving better insights from available datasetsUnderstand and use different Supervised and Unsupervised learning techniques, their biases, how and when to apply them and which methods are the best for a particular analysisWrangle raw data sets or provide data management guidance about data cleaning, such that data is translated into a format that can have advanced methods applied against the resulting dataMaintain responsibility for collaborating with all functions in PharmSci and other divisions to encourage strategic alignment and successful achievement of shared goals, particularly as it pertains to ISF. Leverages technical skill(s) as a resource/expert across PharmSciContribute significantly and independently to project work, which may include multiple projects within a functional areaPlan and implement resolutions to technical problems/issues, recommend and implements technologies and innovationsOwn a discipline/technical skill in its entirety and continues to develop expertise in other key technical skillsIn collaboration with commercial functions, develop and implement strategies to control quality of drug substances and products based on the current GxP regulationsResponsible for authoring relevant sections of regulatory documents, report and peer-reviewed manuscriptsProactively identify vendors and build relationships to gain access to technologies as needed to deliver on pipeline goals.Act as a technical leader for emerging digital capabilities

Requirements: 

Bachelors degree in Data Science, Statistics, Bio-Informatics, Mathematics, Chemometrics, Chemistry, Biology, Engineering or related pharmaceutical science and 11+ years relevant industry experience, orMasters degree in Data Science, Statistics, Bio-Informatics, Mathematics, Chemometrics, Chemistry, Biology, Engineering or related pharmaceutical science 9+ years relevant industry experience, orPhD in Data Science, Statistics, Bio-Informatics, Mathematics, Chemometrics, Chemistry, Biology, Engineering or related pharmaceutical science and 3+ years relevant industry experienceSolid understanding of current Good Manufacturing Practices (cGMP) is preferredExperience in working in a multi-disciplinary team environmentExperience in the use of mathematical, both statistical and first principle, models as well as advanced process control systemsSolid knowledge of the pharmaceutical industry and its R&DExperience in writing regulatory documentsSound knowledge of current GxPComfortable working in the according Data Science environment: using frameworks (e.g. Python, MATLAB, R, JMP), considering industrial Data Engineering protocols (e.g. OPC, Modbus) and repositories (e.g. SQL, Oracle, AWS)Ability to analyze a wide variety of information and data to make management decisions regarding potential risks associated with product quality and regulatory compliance.Demonstratable critical and out-of-the-box thinking abilityUnderstanding of the structure, functions, and methods of the global Takeda organization and overall Takeda R&D operations.Excellent organizational and communication skills. Develops and maintains relationships with colleagues within manufacturing and research networks to identify & support potential value-creating opportunities. Demonstrates ability to negotiate difficult issues and arrive at mutually beneficial solutionsProven scientific track record through presentations at scientific conferences and publication of peer-reviewed manuscriptsSome experience in Project Management

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.

LocationsFujisawa, Japan

Worker TypeEmployee

Worker Sub-TypeRegular

Time TypeFull time