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Job Description
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Takeda Pharmaceuticals is seeking a strategic executive leader to join the R\&D and Global Medical Digital Data \& Technology (DDT) leadership team as Head, Data \& AI Enablement. This pivotal role will define and drive Takeda’s R\&D and Medical AI and Data strategy, with a focus on transformative AI and digital technologies. The head will serve as an inspiring strategic partner to senior leaders across R\&D, provide hands\-on technical leadership at the highest level, and build and develop a high\-performing team from the ground up. As a key architect of R\&D of the Future, our business digital and AI transformation, you will ensure that Takeda remains at the forefront of scientific innovation and operational excellence. This is a tremendous opportunity for a leader who thrives in a global, regulated pharmaceutical environment and is passionate about delivering impact at scale.
How you will contribute:
- AI \& Data Strategy: Develop and deliver a bold multi\-year roadmap for R\&D of the Future AI and data enablement within R\&D and Global Medical DDT, ensuring alignment with Takeda’s business priorities and Enterprise DDT strategy. Lead the adoption of generative AI, large language models, and advanced analytics to address complex scientific and operational challenges.
- Strategic Partnership \& Influence:Serve as a trusted advisor and strategic partner to senior leaders across R\&D and Global medical functions. Inspire alignment and engagement around the DDT transformation agenda, translating complex technical concepts into actionable strategies that support Takeda’s broader R\&D and Medical objectives.
- Technical Leadership:Provide expert guidance and oversight for the design, development, and deployment of advanced AI, machine learning, and data platforms. Ensure technical excellence and operational reliability in all digital and AI initiatives, setting the standard for innovation and delivery within Takeda’s R\&D and Global Medical DDT organization. Champion the integration of emerging technologies, ensuring operational reliability, scalability, and continuous improvement. Demonstrable depth in modern ML/AI methods — able to engage scientists as a peer, not solely as a sponsor or program owner.
- Team Building \& Talent Development:Build, lead, and mentor a world\-class team of data scientists, AI engineers, software developers, and domain experts. Establish a collaborative, high\-performance culture that attracts and develops top talent, enabling the team to deliver on ambitious goals and drive sustained success.
- Operational Excellence \& Technology Integration: Oversee the implementation and scaling of emerging technologies, ensuring robust delivery and integration into research, clinical, and regulatory domains. Drive operational efficiency, reliability, and compliance in all AI and digital initiatives. Accountability for defining or providing measurable and significant input into and operational direction and strategy for R\&D and Global Medical, as well as for successfully integrating these activities with those within Takeda overall.
- Data Ecosystem Stewardship:Lead the development of secure, scalable data platforms, in collaboration with Enterprise partners, that enable seamless access, integration, and thorough utilization of Takeda’s data assets. Champion the use and reuse of data as a strategic currency to drive scientific discovery, accelerate innovation, and unlock new insights across research and clinical domains. Empower teams to leverage AI\-powered analytics for deep exploration, hypothesis generation, and evidence\-based decision\-making, ensuring data is actively harnessed to advance Takeda’s scientific and therapeutic ambitions.
- Governance \& Compliance:Ensure all data and AI initiatives within adhere to global regulatory standards (e.g., HIPAA, GxP), ethical guidelines, and Takeda’s commitment to patient privacy and data integrity.
- Measurement,Reporting\& Accountability: Define and track key performance indicators (KPIs) to measure the impact of AI, data, and digital initiatives. Ensure accountability for delivering measurable business value and continuous improvement.
- External Engagement \& Thought Leadership: Represent Takeda as a thought leader in the external community. Build strategic partnerships with technology vendors, academic institutions, and industry consortia. Present at key conferences and contribute to advancing Takeda’s reputation in AI and digital innovation.
Preferred Qualifications:
- 18\+ years of progressive leadership in digital, data, technology, or R\&D roles within global pharmaceutical, biotechnology, or healthcare organizations.
- PhD or equivalent in Data Science, Computer Science, Artificial Intelligence, Engineering, Bioinformatics, Computational Biology, or related discipline. MBA or advanced business training preferred.
- Proven track record of delivering enterprise\-scale AI and digital solutions in large, regulated environments.
- Deep expertise in emerging technologies (LLMs, generative AI, NLP, advanced analytics) and applying them to drug discovery, translational, clinical development, or regulatory science.
- Success in building and scaling high\-performing teams from inception.
- Experience leading large, diverse, and geographically distributed teams in matrixed organizations.
- Strategic thinker with a focus on operational excellence, talent development, and inclusive leadership.
- Ability to inspire, influence, and align senior leaders across functions and geographies.
- Strong executive presence, communication, and stakeholder management skills.
Takeda Compensation and Benefits Summary
We 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:
$259,000\.00 \- $407,000\.00
The 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\-term incentives. 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 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
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Boston, MAWorker Type
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EmployeeWorker Sub\-Type
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RegularTime Type
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Full timeJob Exempt
Yes
It 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.
Salary Context
This $259K-$407K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Takeda Pharmaceuticals, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $178,940 based on 11,900 positions with disclosed compensation. This role's midpoint ($333K) sits 86% above the category median. Disclosed range: $259K to $407K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Takeda Pharmaceuticals AI Hiring
Takeda Pharmaceuticals has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $407K - $407K.
Location Context
AI roles in Boston pay a median of $213,400 across 422 tracked positions. That's 7% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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