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Job Summary\ The Senior Director, AI and Data Science Solutions will be a visionary and strategic leader responsible for defining and executing the AI, Generative AI, and Data Science solutions strategy across the entire R\&D value chain with a deep knowledge of AI, Generative AI and R\&D Data, R\&D workflows, clinical trial planning and execution. This role will empower scientific, as well as business functions—including drug development, clinical trials, real\-world data evidence—by delivering or building robust, scalable, and compliant AI and data science solutions across drug development and implementing advanced data, AI and Generative AI (Gen AI) solutions.As a senior technical leader, the Senior Director will manage a high\-performing team of AI and data scientists, machine learning scientists and specialist developers to foster a culture of innovation, and ensure the adoption of best\-in\-class AI solutions practices within a regulated pharmaceutical environment. The leader will bridge the gap between scientific ambition and technical execution, translating complex R\&D challenges into impactful data and AI solutions. The role will oversee a team, manage resources and overall operation and execution of portfolio while collaborate with DSAI and DnA leadership, internal and external partners to deliver high\-quality data and analytic products.The AI and Data Science Solutions team will focus on establishing and enhancing Otsuka’s Data and AI solutions foundations on top of the AI platform provided by Data and AI platform team in IT and working closely with AI and Data Engineering team in Data Science and AI function. This role will oversee team is a centralized capability and comprises of programmers, full\-stack AI and data scientist who work closely with other Data and Analytics, AI engineers, Data Engineers, various roles in IT, and other business functions.\ Job Description\ Strategy and VisionDefine and drive the strategic vision and roadmap for AI, Gen AI capabilities, solutions, use cases, and larger connected AI programs within R\&D, aligning with overall business objectives and scientific priorities.Serve as a key thought leader and trusted advisor to senior R\&D leadership on opportunities and risks related to AI and data technologies and Gen AI. Represent in key AI and Data solutions initiatives in Otsuka as needed.Deliver leading edge AI solutions approaches, effective enterprise AI and data strategy (specifically clinical, operational, and real\-world data), robust and responsible AI/ML model development, fit\-for\-purpose clinical development data stewardship strategies to enable key data insights to inform decision making and assist in business transformation initiatives.Evaluate and adopt emerging data and AI technologies to create best\-in\-class scalable and robust AI solutions, balancing new opportunities with the need for scalability, reliability, and regulatory compliance.\ Execution and Technical LeadershipLead and deliver the design, development, and pre\-deployment of extensible and scalable AI solutions for complex R\&D Data, and solution architecture for AI applications to support all R\&D data AI needs, including structured, unstructured (text, imaging, and data from sensors, medical devices, and digital apps), and real\-time data.Lead quick feasibilities of AI solutions with DSAI portfolio as well as act as an advisor to the rest of the organization on AI solutions, pros and cons of approaches, various solution architecture choices, as needed.Oversee the implementation of AI and Gen AI use cases from ideation to production deployment, and application life cycle management working closely with AI and Data Engineering team. Hands on experience in architecting AI and Gen AI solutions, foundation for a diverse set of AI solutions.Lead and deliver data processing, representations, and AI solutions in R\&D for applications ranging across: clinical trial optimization, patient recruitment, content authoring automation, asset management, pharmacovigilance, quality, regulatory, and enterprise functions, Real\-World Data, sensor and medical device data, omics data.Champion feasibility and AI Solutions best practices, review AI solution architectures, and data \& AI observability to ensure technical consistency, scalability, and effective governance of AI engineering across the portfolio of oversight.Architect and scale AI and ML practices for in house applications to support the entire model lifecycle—from experimentation and deployment to monitoring and retraining—while ensuring automation and reproducibility.Drive awareness of AI/ML applications and the importance of strong Data and AI engineering foundation across the organizationProvide technical and engineering support for Data and Information Governance, quality, and FAIR data\ Team and Organizational LeadershipBuild, mentor, and lead a high\-performing and collaborative team of data scientists, AI/ML scientists, life cycle support.Foster a culture of innovation, continuous learning, accountability, cohesiveness, and respect, within and across teams, and stakeholders.Effectively manage the team, budget, resource allocation, and project timelines for the data and AI Solutions portfolio.\ Oversight and GovernanceEnsure that all data and AI initiatives and applications adhere to internal governance standards, data privacy regulations (e.g., GxP, HIPAA), and responsible AI principles.Collaborate with Legal and Compliance teams to embed auditability and traceability into AI and Gen AI workflows.Serve as AI and Data Solutions expert for internal portfolio as well as organization wide initiatives, reviews, and AI committees.Establish and manage robust monitoring, alerting, and incident response processes for all AI capabilities and solutions within the remit.Effectively manage and provide oversight on vendors and vendor resources providing support to data and AI engineering portfolioLead or participate in meetings related to Data or AI, its impact, enablement, governance, determining or enabling roadmaps and strategy as needed.\ Cross\-functional CollaborationWork closely with Data and AI Platform team and leverage platform level capabilities, solutions provided by IT as well as share data and AI solution needs as well as guidance with respective providers (Data and AI platform, IT infrastructure, etc.)Build strong, trusted relationships with key stakeholders across R\&D, Scientific and Operations AI Data Science teams, other DnA pillars, IT, and external partners.Partner with R\&D teams to translate complex scientific challenges into clear, executable data and AI projects.Communicate strategy, progress, and outcomes to diverse stakeholders, including technical teams and executive leadership.Lead as subject matter expert, while communicating, and collaborating effectively to ensure portfolio success.\ Qualifications/ Required\ Knowledge/ Experience and Skills:12\+ years of progressive leadership experience in data science, AI/ML, and Gen AI with at least 5 years in senior technical leadership or management roles with strong team management and development.Strong and demonstrated experience in architecting, building and maintaining large\-scale data and AI solutions in a scientific, regulated, or research\-heavy environment.Strong experience working within the pharmaceutical, biotech, or life sciences industry, particularly within R\&D, is highly desirable along with a deep understanding of AI and Machine Learning and its applications in Pharma.Proven track record of implementing and deploying Gen AI and large language model (LLM) applications in production environments.Creative problem solving using responsible use of technology.Expertise in real\-world data assets and using them to generate scientific evidence and guide operational effectiveness and efficiencies.Deep expertise across data science, representation, Gen AI, AI and machine learning techniques and experience in architecting and delivering AI/ML use cases.Strong team leadership, internal and cross\-functional collaboration, project management skills with a focus on delivering impactful initiatives.Educational QualificationsMasters in Mathematical, Engineering, or Scientific disciplines such as Computer Science, Mathematics, Engineering, Physics, Statistics, or a related field with focus on advanced and modern Data Science, including the use of AI and machine learning. PhD is strongly preferred.CompetenciesAccountability 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 $230,720\.00 \- Maximum $345,000\.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://ah\-prod.com/multi/videoplayer/player.html?v\&\#61;6385356920112\.Join Our Talent Community: Stay connected with Otsuka by joining our Talent Community. Tell us about your interests, and we’ll notify you about future opportunities that align with your background. https://community.hireez.com/cielo\-otsuka/82cb3591Disclaimer: 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 EEAccommodations\&\#64;otsuka\-us.com. Statement Regarding Job Recruiting Fraud ScamsAt Otsuka we take security and protection of your personal information very seriously. Please be aware individuals may approach you and falsely present themselves as our employees or representatives. They may use this false pretense to try to gain access to your personal information or acquire money from you by offering fictitious employment opportunities purportedly on our behalf.Please understand, Otsuka will never ask for financial information of any kind or for payment of money during the job application process. We do not require any financial, credit card or bank account information and/or any payment of any kind to be considered for employment. We will also not offer you money to buy equipment, software, or for any other purpose during the job application process. If you are being asked to pay or offered money for equipment fees or some other application processing fee, even if claimed you will be reimbursed, this is not Otsuka. These claims are fraudulent and you are strongly advised to exercise caution when you receive such an offer of employment.Otsuka will also never ask you to download a third\-party application in order to communicate about a legitimate job opportunity. Scammers may also send offers or claims from a fake email address or from Yahoo, Gmail, Hotmail, etc, and not from an official Otsuka email address. Please take extra caution while examining such an email address, as the scammers may misspell an official Otsuka email address and use a slightly modified version duplicating letters.To ensure that you are communicating about a legitimate job opportunity at Otsuka, please only deal directly with Otsuka through its official Otsuka Career website https://vhr\-otsuka.wd1\.myworkdayjobs.com/en\-US/External.Otsuka will not be held liable or responsible for any claims, losses, damages or expenses resulting from job recruiting scams. If you suspect a position is fraudulent, please contact Otsuka’s call center at: 800\-363\-5670\. If you believe you are the victim of fraud resulting from a job recruiting scam, please contact the FBI through the Internet Crime Complaint Center at: https://www.ic3\.gov, or your local authorities.Otsuka America Pharmaceutical Inc., Otsuka Pharmaceutical Development \& Commercialization, Inc., and Otsuka Precision Health, Inc. 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Salary Context
This $230K-$345K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($287K) sits 34% above the category median. Disclosed range: $230K to $345K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Otsuka AI Hiring
Otsuka has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $345K - $345K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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