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About This Role
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Job Description
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Takeda is on an exciting journey to transform the organization by harnessing the power of data, analytics, artificial intelligence, agentic and intelligent automation. The Head of AI and Data Science will set and drive the strategic direction, execution, and enterprise impact of Takeda’s AI Forward programs and AI initiatives (Agentic, Generative, and Data Science), translating these capabilities into an AI\-enabled organization at scale.
Reporting to the Head of AI and Data within the Data, Digital and Technology (DD\&T) organization, this leader will shape and oversee the AI roadmap from concept through delivery across the Takeda Pharma Value Chain, BioLife Value Chain processes, and enterprise programmatic solutions.
The role combines visionary technology leadership with hands\-on execution, ensuring AI initiatives are prioritized, delivered at scale, and embedded into business workflows.
Working closely with business and technology stakeholders, this leader will ensure AI solutions are aligned to strategic priorities, adopted by end users, and measured against clear business outcomes.
The role will uphold Takeda’s standards for responsible, compliant, secure, and trustworthy AI throughout the solution lifecycle.
Driving Takeda’s AI Transformation
- Lead Takeda’s enterprise AI strategy and roadmap, translating business priorities into scalable AI\-enabled capabilities that create measurable impact across the organization.
- Shape the direction, operating model, and execution cadence for enterprise AI initiatives, ensuring focus, accountability, and sustained value delivery.
- Build and lead an AI Center of Excellence that serves as a catalyst for transformation by sharing best practices, enabling communities of practice, and mobilizing cross\-functional expertise.
- Connect strategy to execution through clear value cases, adoption goals, outcome metrics, and compelling executive communication.
Scaling AI Across the Enterprise
- Guide a portfolio of transformational AI initiatives (Game Changing and Process AI) across agentic AI, generative AI, intelligent automation, and data science, from ideation through deployment and enterprise scaling.
- Champion the use of AI to improve productivity, decision\-making, customer experience, and operational effectiveness across Takeda’s global business functions with People experience function.
- Promote innovation through AI Labs, rapid experimentation, pilots, and MVPs, while scaling successful solutions into sustainable production capabilities.
- Co\-Own the end\-to\-end AI product lifecycle with Head of AI \& Data Products, including selection of the technology stack, data organization, context and prompt engineering, and model training.
- Partner with Head of AI and Data Governance to evolve enterprise AI \& Data governance structures and processes.
- Partners with Data \& AI engineering, architecture, data platforms, MLOps, AIOps, and LLMOps teams to establish reusable patterns for secure, reliable, and scalable AI delivery.
- Own practice level financials, including revenue, pipeline development, profitability and operating model design
- Ensure AI capabilities are scalable, reliable, and embedded effectively into operational processes. Evaluate and manage third\-party vendors, tools, and platforms related to AI, automation, and data infrastructure. Oversee all BI, AI, Reporting and Visualization processes and governance.
Collaborating Across Takeda
- Work closely with business, product, technology, data science, risk, legal, compliance, privacy, quality, security, and external partner teams to embed AI into everyday ways of working.
- Engage executive leaders, councils, and cross\-functional teams to build alignment, secure investment, and create shared ownership for AI\-enabled transformation.
- Partner with domain experts and business units to identify high\-value opportunities where AI can accelerate decision\-making, automation, and innovation.
- Communicate AI strategy, progress, outcomes, and lessons learned in a clear and compelling way that inspires adoption across the enterprise.
Advancing Responsible, Trusted AI
- Advance responsible, compliant, secure, and trustworthy AI practices across the solution lifecycle.
- Establish practical guardrails for model risk management, transparency, human oversight, ethical use, and enterprise control of agentic AI capabilities.
- Manage AI capabilities as enduring products, with clear roadmaps, user experience standards, adoption measures, and structured lifecycle governance.
- Continuously monitor emerging AI technologies and translate relevant advances into practical opportunities that support Takeda’s mission and business priorities.
- An advanced degree (M.S., PhD.) in mathematics, applied statistics, computer science, machine learning or similar
Education and Competencies
- 15\+ years of experience in technology strategy, data analytics, or intelligent automation, with at least 3 years leading enterprise\-level AI or digital transformation initiatives
- Proven experience in Pharma Industry
- Proven success in designing and maintaining AI, GenAI, Agentic AI systems at scale in production environments
- Deep expertise in modern AI and Agentic AI infrastructure and enterprise\-grade system design
- Deep understanding of AI/ML technologies, Generative AI and Agentic platforms and Skills (e.g., Microsoft CoPilot, LLMs, Memory and knowledge management, planning and reasoning, multi\-agent orchestration, Advance Prompt engineering)
- Business acumen and financial management skills
- AI Agent \& LLM Experience: Demonstrated experience building AI agents, working with LLMs, and applying agentic patterns to automate complex workflows and business processes
- Continuous learning mindset and ability to adapt to changing market dynamics.
- Leadership: Ability to inspire and guide teams towards achieving product goals
- Communication \& Storytelling: Ability to articulate transformation narratives that connect AI capabilities to business outcomes, making complex technical concepts accessible to executives, product leaders, and engineering teams alike.
- Emotional Intelligence (EQ): Build strong relationships and foster a collaborative environment.
- Problem\-Solving: Identify challenges and find innovative solutions.
- Strategic thinker with strong conceptualization skills, yet flexible and nimble in thinking and approach to problem solving.
- Demonstrate a can\-do attitude and the ability to define success w/a bias towards action.
- Experience working within a complex organization and demonstrated ability to work across functions and regions, at all levels where the incumbent may not have direct authority.
- A self\-starter attitude, thriving in dynamic environments, demonstrating ownership, accountability, and an ability to juggle multiple priorities
- Experience with data management and governance solutions such as Informatica or Collibra.
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:
Cambridge, MAU.S. Base Salary Range:
$212,000\.00 \- $333,190\.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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Cambridge, 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 $212K-$333K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 Required
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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($272K) sits 25% above the category median. Disclosed range: $212K to $333K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; 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 Cambridge, MA, US. Compensation range: $333K - $333K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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