Associate Director - AI/ML (R&D)
Company: Takeda
Location: Boston
Posted on: June 1, 2025
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 DescriptionAt Takeda, we are a forward-looking,
world-class R&D organization that unlocks innovation and
delivers transformative therapies to patients. By focusing R&D
efforts on three therapeutic areas and other targeted investments,
we push the boundaries of what is possible to bring life-changing
therapies to patients worldwide.Objective / Purpose:Takeda is
seeking an Associate Director to join our AI/ML & Data team in
Boston, MA. This technical role focuses on implementing AI-driven
drug discovery solutions across Takeda's key therapeutic areas and
modalities, including small molecules and biologics. As a technical
expert within our computational biology, chemistry, and data teams,
you will build and deploy state-of-the-art AI/ML technologies and
mathematical models to accelerate target identification,
validation, and drug discovery workflows. This execution-focused
role offers the opportunity to develop advanced AI platforms and
implement novel approaches, such as agentic systems and reasoning
models, to enhance discovery efforts across oncology, neuroscience,
and inflammatory diseases.Accountabilities:
- Build AI Solutions for Target Discovery: Develop and deploy
AI/ML systems for target identification and validation in oncology,
neuroscience, and GI - initiatives for small molecules and
biologics. Process and analyze large-scale datasets to uncover
novel therapeutic opportunities and biomarkers.
- Engineer Agentic Systems & Reasoning Models: Create and
implement advanced AI systems, including agentic AI (e.g.,
multi-agent models, reinforcement learning) to automate hypothesis
generation, experimental design, and data analysis, enabling
efficient small molecule and biologic drug discovery.
- Develop AI-Integrated Tools: Build and maintain AI/ML models
that integrate biological, chemical, and omics data, ensuring
computational outputs provide actionable insights for drug
optimization.
- Implement Machine Learning Models: Code and deploy
state-of-the-art machine learning algorithms, including deep
learning, graph-based models, and active learning approaches, to
power in silico screening, molecule design, and biological
predictions for oncology, neuroscience, and GI - drug discovery.
- Build Knowledge Graphs & Foundation Models: Develop and
maintain knowledge graph technologies and foundation models (e.g.,
language models) that integrate diverse data sources (omics,
literature), supporting scientific reasoning and hypothesis testing
across drug discovery workflows.
- Execute Cross-Functional Deliverables: Collaborate with
computational biology, chemistry, and digital sciences teams to
implement AI solutions within experimental workflows. Ensure model
outputs are production-ready and provide tangible insights across
oncology, small molecule, biologics, and GI - initiatives.
- Develop AI Research Tools: Create and optimize AI-enhanced
research tools for small molecule and biologic discovery. Build
novel AI/ML implementations that can generate intellectual
property.
- Technical Mentorship: Provide practical technical guidance to
team members, demonstrating best practices in coding, model
development, and AI implementation across Takeda.
- Technical Documentation & Communication: Document AI system
architectures and model implementations effectively. Present
technical solutions to scientific stakeholders to support
decision-making across Takeda's R&D efforts.
- Educational Background: Ph.D. in Computer Science, Data
Science, AI, Computational Biology, or related field preferred (or
M.S. with significant relevant experience). Strong practical coding
skills and proven experience building AI/ML systems for drug
discovery.
- Technical AI/ML Expertise: 8+ years of experience building and
deploying AI/ML or mathematical modeling solutions for drug
discovery challenges. Demonstrated success implementing
production-level systems independently. Direct experience coding
novel AI systems (e.g., agentic systems, reasoning models) is
highly advantageous.
- Proven Development Track Record: Extensive experience writing
production code for machine learning systems (e.g., deep learning,
reinforcement learning, graph models, active learning) in drug
discovery settings.
- Applied Computational Experience: Practical experience
implementing AI/ML models for small molecule and biologic drug
discovery, with proven ability to create functional tools that
translate computational outputs into experimental insights.
Experience in oncology, neuroscience or GI - therapeutic areas is
advantageous.
- Technical Stack Expertise: Advanced proficiency in Python, with
experience building on cloud platforms (AWS, Azure, or GCP), and
implementing solutions using machine learning frameworks (e.g.,
TensorFlow, PyTorch).
- Execution & Collaboration: Track record of successfully
delivering AI/ML projects from concept to production within
cross-functional teams. Demonstrated ability to implement working
solutions that drive drug discovery programs.
- Technical Innovation & Documentation: History of developing
novel AI implementations in scientific research, coupled with
strong abilities to document and explain technical architectures to
diverse audiences across the organization.EDUCATION, BEHAVIOURAL
COMPETENCIES AND SKILLS:
- PhD degree in a Computer Science, Data Science, AI,
Computational Biology, or related field preferred with 7+ years
experience , or MS with 13+ years experience, or BS with 15+ years
experience
- Strong practical coding skills and proven experience building
AI/ML systems for drug discoveryTechnical AI/ML Expertise:
preferably 8+ years of experience building and deploying AI/ML or
mathematical modeling solutions for drug discovery challenges.
Demonstrated success implementing production-level systems
independently. Direct experience coding novel AI systems (e.g.,
agentic systems, reasoning models) is highly advantageous.
- Proven Development Track Record: Extensive experience writing
production code for machine learning systems (e.g., deep learning,
reinforcement learning, graph models, active learning) in drug
discovery settings.
- Applied Computational Experience: Practical experience
implementing AI/ML models for small molecule and biologic drug
discovery, with proven ability to create functional tools that
translate computational outputs into experimental insights.
Experience in oncology, neuroscience or GI - therapeutic areas is
advantageous.
- Technical Stack Expertise: Advanced proficiency in Python, with
experience building on cloud platforms (AWS, Azure, or GCP), and
implementing solutions using machine learning frameworks (e.g.,
TensorFlow, PyTorch).
- Execution & Collaboration: Track record of successfully
delivering AI/ML projects from concept to production within
cross-functional teams. Demonstrated ability to implement working
solutions that drive drug discovery programs.
- Technical Innovation & Documentation: History of developing
novel AI implementations in scientific research, coupled with
strong abilities to document and explain technical architectures to
diverse audiences across the organization.If you are ready to be
part of a forward-thinking, engineering-driven team at Takeda,
contributing to transformative innovations in drug discovery
through technical implementation, we encourage you to apply for
this Associate Director role.Takeda Compensation and Benefits
SummaryWe understand compensation is an important factor as you
consider the next step in your career. We are committed to
equitable pay for all employees, and we strive to be more
transparent with our pay practices.For Location:Boston, MAU.S. Base
Salary Range:$153,600.00 - $241,340.00The estimated salary range
reflects an anticipated range for this position. The actual base
salary offered may depend on a variety of factors, including the
qualifications of the individual applicant for the position, years
of relevant experience, specific and unique skills, level of
education attained, certifications or other professional licenses
held, and the location in which the applicant lives and/or from
which they will be performing the job.The actual base salary
offered will be in accordance with state or local minimum wage
requirements for the job location.U.S. based employees may be
eligible for short-term and/or long-termincentives. U.S.based
employees may be eligible to participate in medical, dental, vision
insurance, a 401(k) plan and company match, short-term and
long-term disability coverage, basic life insurance, a tuition
reimbursement program, paid volunteer time off, company holidays,
and well-being benefits, among others. U.S.based employees are also
eligible to receive, per calendar year, up to 80 hours of sick
time, and new hires are eligible to accrue up to 120 hours of paid
vacation.EEO 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 timeJob ExemptYesIt is unlawful in
Massachusetts to require or administer a lie detector test as a
condition of employment or continued employment. An employer who
violates this law shall be subject to criminal penalties and civil
liability.
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Keywords: Takeda, Providence , Associate Director - AI/ML (R&D), Executive , Boston, Rhode Island
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