Sr. Manager, Data Scientist - Automation
Company: Takeda Pharmaceutical
Location: West Orange
Posted on: May 11, 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 and . I further attest that all information I submit
in my employment application is true to the best of my
knowledge.Job DescriptionOBJECTIVES/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 ecosystemACCOUNTABILITIES:
- 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 companyCORE 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 organizationDIMENSIONS 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.LeadershipAbility 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 updatesComplexity
- 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 environmentEEO StatementTakeda 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.LocationsBoston, MAWorker TypeEmployeeWorker
Sub-TypeRegularTime TypeFull time
Keywords: Takeda Pharmaceutical, West Orange , Sr. Manager, Data Scientist - Automation, Executive , West Orange, New Jersey
Didn't find what you're looking for? Search again!
Loading more jobs...