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Sr. Manager, Data Scientist - Automation

Company: Takeda Pharmaceutical
Location: Clifton
Posted on: May 12, 2022

Job Description:

By clicking the "Apply" button, I understand that my employment application process with Takeda will commence and that the information I provide in my application will be processed in line with Takeda's Privacy Notice and Terms of Use . I further attest that all information I submit in my employment application is true to the best of my knowledge.

Job Description
OBJECTIVES/PURPOSE:

Takeda is undertaking a transformation of clinical development to increase effectiveness and efficiency, thereby bringing critical therapeutics to patients faster. Takeda's Clinical Data Strategy is a key part of this effort and will require significant organizational and operating model changes to implement.
The mission of the project is to deliver an automated, integrated, in-house clinical data pipeline, a single source of truth across the clinical data lifecycle, offering teams near real-time access to the data they need, when they need it, built on a foundation of quality, security, and compliance.
As such, the Data Scientist for clinical trial data automation will have to:
Assesses use cases technical feasibility and impact on the clinical trial lifecycle; partners with data engineers to create and implement innovative solutions.
Independently perform complex analyses using modern Data Science techniques (e.g. Machine Learning, Deep Learning, and others) against structured or unstructured data and report results back to generate insights to Takeda.
Deliver critical analysis against Takeda's toughest clinical trial data problems to provide critical insight to the organization's largest questions.
Owns the final products and partners with relevant function to deploy and maintain it in production within the system and architecture for clinical trial data ecosystem

ACCOUNTABILITIES:
Leverage advances machine learning, deep learning and other advanced data techniques to create cutting-edge algorithms for automating clinical trial data processing.
Introduce novel and state-of-the-art computational techniques to other teams and scientists to improve capabilities for data analysis with the purpose of deriving high accuracy insight from available datasets more efficiently.
Understanding and usage of different Supervised and Unsupervised learning techniques, their biases, how and when to apply them and which methods are the best for a particular analysis.
Ability to wrangle raw data sets into a format that can have advanced methods applied against the resulting data.
Work independently on tough problems with other team members and independently solve, with some guidance, very difficult technology and data problems.
Demonstrate advanced tooling and techniques to other technical organizations throughout the company

CORE ELEMENTS RELATED TO THIS ROLE:
Provide leadership and expertise to best construct data and execute analysis for feature detection, retrospective and predictive modelling, within a complex R&D and Vaccines environment.
Understanding of Machine Learning, Deep Learning, Re-enforcement learning and other techniques to drive automation in clinical data management.
Ability to stay up to speed on modern data techniques, understand how to apply them and constantly demonstrated how and where to apply these new methods.
Deep knowledge and understanding of data security and privacy to maintain GxP compliance for any algorithm and script developed.
Design, Execute, QC and Deliver Data Science Analysis independently and effectively across the organization

DIMENSIONS AND ASPECTS:

Technical/Functional (Line) Expertise
Work independently on tough problems with other team members and independently solve, with some guidance, very difficult technology problems.
Maintains up-to-date knowledge on modern technologies, explores new platforms and beta tooling.
Apply advanced techniques to complex problems in R&D and other organizations.
Apply modern mathematical methods for data analysis.

Leadership
Ability to drive new Data Science capabilities in the organization. - This includes understanding a new method/technology, knowing what it may be good at and demonstrating a valid usage of this methods against the appropriate data problem.
Mentoring other Data Teams in usage of technology and methods across the organization as we constantly grow our capabilities
Being the example for other data teams on analysis precision, output, quality and method selection for data science analysis for our team and others as well.

Decision-making and Autonomy
Determine what methods are best for specific analysis engagements in order to drive to a design as well as determining their bias.
Ability to determine what technology and methods can be combined for the optimal result on an ever-changing product landscape.
Drive to where the Data Leads and keep analysis aligned with unbiased analysis of the data.

Interaction
While performing an analysis, coordinating with other data specialists while presenting to Executive (VP+) level audience across multiple organizations.
Coordinate with Data Scientists, Data Engineers, Statisticians, Computational Biologists, other Data Specialists and business end-users across the product ecosystem.
Strong communication and the ability to clearly convey information both in the group and to external groups.
Ability to conduct high level conversations with internal partners as well as external collaborators.

Innovation
Ability to understand complex problems and be able to apply modern technology patterns to them.
Ability to influence technical directions both internally to applied data engagements.
Ability to keep up to speed on the latest methods in data analysis. - This will include the machine learning, deep learning, reinforcement learning and the continuing new updates

Complexity
Ability to distill complex product feedback into actionable strategies, implementations and stable deployments.
Ability to have an agile time-based delivery team operate within a traditional project and waterfall-based funding and approval cycles.
Ability to derive high accuracy insight from unstructured, incomplete or large data sets when traditional techniques do not work.

EDUCATION, BEHAVIOURAL COMPETENCIES AND SKILLS:
Master's Degree or PhD in Computer Science, Data Science or equivalent
3+ years' experience or a PhD and relevant project / coursework
Expertise with the Application of Machine Learning and / or Deep Learning
Up-to-date specialized knowledge of data wrangling, manipulation and management of technologies
Experience with Amazon Web Services
Ability to manipulate voluminous data with different degree of structuring across disparate sources to build and communicate actionable insights for internal or external parties
Possesses strong personal skills to portray information
Ability to work in an agile and rapid changing environment with high quality deliverables
Experience with two of the following languages: - Python, R, Java or Scala
Experience with deep learning frameworks: - TensorFlow, MX Net
Working knowledge of SQL and NoSQL datastores
Experience in a scientific environment

EEO Statement
Takeda is proud in its commitment to creating a diverse workforce and providing equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, gender expression, parental status, national origin, age, disability, citizenship status, genetic information or characteristics, marital status, status as a Vietnam era veteran, special disabled veteran, or other protected veteran in accordance with applicable federal, state and local laws, and any other characteristic protected by law.

Locations
Boston, MA

Worker Type
Employee
Worker Sub-Type
Regular
Time Type
Full time

Keywords: Takeda Pharmaceutical, Clifton , Sr. Manager, Data Scientist - Automation, Executive , Clifton, New Jersey

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