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The Director of AI Adoption and Enablement is a strategic and operational leader in Otsuka’s Data and Analytics organization responsible for facilitating adoption, and governance of AI through AI Enabling committee and its sub\-committees across the OAPI/OPDC enterprise as well as with specific affiliates. This role will be instrumental in maintaining visibility through the lifecycle of the AI Use Cases including ideation, development, ownership, deployment, measurement and enhancements. In addition to maintaining the AI use case repository in collaboration with IT Business Partners, this role will support activities that promote AI awareness, AI literacy, AI adoption, and adherence to AI guidelines. As an ongoing practice, the role will ensure metrics of business impact and their uniform collection is performed across the portfolio and participate in assessment that responsible AI practices are followed within the use cases.
The Director of AI Adoption and Enablement will act as a strategic bridge in DnA supporting AI Enablement committee to coordinate between Data Analytics, IT, business functions, vendors and end users, driving cultural transformation, developing comprehensive enablement programs, and coordinating governance to facilitate responsible and compliant development and use of AI solutions at scale in Data and Analytics portfolio. To ensure success of AI adoption and enablement, the role will coordinate AI governance, AI enablement meetings, AI use case assessments across stages, and deeply support communications, AI adoption and training programs, directly support or maintain alignment with other Enterprise initiatives as well as initiatives that are central to Data and Analytics. To ensure the success of these efforts, this role will be accountable in Data and Analytics, specifically Data Science and AI for the change management necessary to ensure cultural change, literacy, awareness and adoption. The role will partner closely with Enterprise Change Management to ensure alignment with the broader enterprise messaging, change mechanisms, and practices.
The Director of AI Adoption and Enable ment is responsible for coordinating to support Data and Analytics Data Science and AI portfolio and AI E nablement and supporting operationaliz ation of AI strategy through use cases, their adoption, value tracking, and trainings by working as a bridge between Data and Analytics and business functions . This individual will serve as a strategic bridge between Data Analytics, IT, and business functions supported by Data and Analytics, as well as coordinat e activities needed in AI Enabling committee .
- Coordinati ngStrategic roadmap for AI Enablement and Adoption: Develop a plan and lead the operationalization of AI awareness, enablement and adoption roadmap, ensuring it s align ment with Otsuka ’s AI strategy , measuring and communicating their effectiveness
- Lead coordination of AI Governance: Lead coordination various AI Governance meetings, including AI Enablement Committee , AI Science and Applications Council , Data and AI Platform Council, and use case assessment and reviews , AI communication (outside\-in and internal initiatives)
- AI use casesCoordination : Coordinate use case assessment, architecture reviews, maintain a catalog of use cases and their status across their life cycle in collaboration with the team responsible for use cases , status and impact metrics, help assess and track user feedback , and strategic communication related to their adoption and refinements
- Coordinati ngAI Governance activities acrossAI use cases: Track use case activities through stage gates (proof\-of\-concept, pilot, full production deployment), managing end\-to\-end coordination of ensuring stakeholder engagement throughout the process and escalating to appropriate channels where needed
- Performance Monitoring: With stakeholders, d efine and track key performance indicators (KPIs) to measure the ROI and effectiveness of AI initiatives and use cases across Otsuka and us e data\-driven insights to refine strategies and demonstrate business value . Facilitate engagement with end\-users to ensure customer\-centric approaches.
- Compliance with Responsible AI and other guidelines : Contribute to operationalization of framework for responsible AI across use cases , such that there is compliance with regulations like GDPR, HIPAA, and the EU AI Act. This includes contributing to documenting guidance issued by ASAP council for responsible AI, implementing a system of record for AI assets, and coordinating closely with Legal, Security, and Risk to manage deployment risks.
- Execution and Operations : M onitor key performance indicators (KPIs) for adoption and ROI, and coordinate with IT, data science, and product teams.
- Enablement and CulturalTransformationforAIusein the scope of Data Science and AI in Data and Analytics : Working with Change Management teams, AI enablement programs and networks to train teams and encourage data\-driven decision\-making. Act as a change agent aligned with organizations change management strategy, teams, and guidelines , u nderstand and implement mechanisms and change management approaches to ensure the adoption of AI tools and capabilities through various programs and communication strategies to foster a culture of AI fluency, awareness , and innovation which are needed for successful AI adoption and transformation
- Align with or lead coordination of initiativesadjacent toAIinDataAnalytics portfolio : Maintain alignment with or lead coordination of initiatives that support AI, such Data, Data strategy, Real World Data Strategy, Analytics or their governance. Maintain and communicate metrics around their impact and escalate to appropriate bodies as needed
Qualifications / Requirements:
- Education: A Bachelor's degree is required , with a Master's preferred . An MBA is a plus.
- O ver 10 years of experience in a mix of Pharma drug development or commercialization and enterprise digital transformation or technology adoption, with at least 7 years in biopharmaceuticals and experience integrating technology a regulated setting. E xperience with AI/ML concepts and technology transformation is beneficial.
- Excellent coordination, project management, change management and stakeholder management skills with demonstrated successful experience in delivering complex multi\-year Enterprise initiatives or projects
- Broad understanding of drug development and R\&D organization and associated processes.
- Excellent understanding of Pharma business, processes, and regulations to effectively deliver on AI transformation
- Strong communication skills are needed to explain complex AI to non\-technical stakeholders, along with leadership, collaboration, and strategic thinking abilities. Knowledge of AI technologies and pharmaceutical regulations is also important.
- Experience collaborating with cross\-function teams, such as data science , AI , and IT teams and a familiarity with concepts in AI , Data, Analytics, Data and Information Governance, etc.
- Excellent leadership, communication, and change management skills, with the ability to influence senior stakeholders across a complex, matrixed organization.
Competencies
Accountability for Results \- Stay focused on key strategic objectives, be accountable for high standards of performance, and take an active role in leading change.
Strategic Thinking \& Problem Solving \- Make decisions considering the long\-term impact to customers, patients, employees, and the business.
Patient \& Customer Centricity \- Maintain an ongoing focus on the needs of our customers and/or key stakeholders.
Impactful Communication \- Communicate with logic, clarity, and respect. Influence at all levels to achieve the best results for Otsuka.
Respectful Collaboration \- Seek and value others’ perspectives and strive for diverse partnerships to enhance work toward common goals.
Empowered Development \- Play an active role in professional development as a business imperative.
Minimum $186,489\.00 \- Maximum $278,875\.00, plus incentive opportunity: The range shown represents a typical pay range or starting pay for individuals who are hired in the role to perform in the United States. Other elements may be used to determine actual pay such as the candidate’s job experience, specific skills, and comparison to internal incumbents currently in role. Typically, actual pay will be positioned within the established range, rather than at its minimum or maximum. This information is provided to applicants in accordance with states and local laws.
Application Deadline : This will be posted for a minimum of 5 business days.
Company benefits: Comprehensive medical, dental, vision, prescription drug coverage, company provided basic life, accidental death \& dismemberment, short\-term and long\-term disability insurance, tuition reimbursement, student loan assistance, a generous 401(k) match, flexible time off, paid holidays, and paid leave programs as well as other company provided benefits.
Come discover more about Otsuka and our benefit offerings; https://www.otsuka\-us.com/careers\-join\-otsuka .
Disclaimer:
This job description is intended to describe the general nature and level of the work being performed by the people assigned to this position. It is not intended to include every job duty and responsibility specific to the position. Otsuka reserves the right to amend and change responsibilities to meet business and organizational needs as necessary.
Otsuka is an equal opportunity employer . All qualified applicants are encouraged to apply and will be given consideration for employment without regard to race, color, sex, gender identity or gender expression, sexual orientation, age, disability, religion, national origin, veteran status, marital status, or any other legally protected characteristic .
If you are a qualified individual with a disability or a disabled veteran, you may request a reasonable accommodation, if you are unable or limited in your ability to apply to this job opening as a result of your disability . You can request reasonable accommodations by contacting Accommodation Request .
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Otsuka America Pharmaceutical Inc., Otsuka Pharmaceutical Development \& Commercialization, Inc., and Otsuka Precision Health, Inc. (“Otsuka”) does not accept unsolicited assistance from search firms for employment opportunities. All CVs/resumes submitted by search firms to any Otsuka employee directly or through Otsuka’s application portal without a valid written search agreement in place for the position will be considered Otsuka’s sole property. No fee will be paid if a candidate is hired by Otsuka as a result of an agency referral where no pre\-existing agreement is in place. Where agency agreements are in place, introductions are position specific. Please, no phone calls or emails.
Salary Context
This $186K-$278K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Otsuka, 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 $185,000 based on 13,200 positions with disclosed compensation. Director-level AI roles across all categories have a median of $250,000. This role's midpoint ($232K) sits 26% above the category median. Disclosed range: $186K to $278K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Otsuka AI Hiring
Otsuka has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Princeton, NJ, US. Compensation range: $278K - $278K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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