Associate Director, AI Clinical Scientist

Otsuka Pharmaceutical Co., Ltd.•Princeton, NJ

6d•$169,222 - $253,000

About The Position

The Associate Director, AI Clinical Scientist on Otsuka’s Data Science and AI team leads strategic and technical development and application of AI solutions across scientific domains in Otsuka R&D. This technical role applies traditional and generative AI to advance drug development and evidence generation. The position works closely with stakeholders and cross-functional teams to support Data Science and AI objectives aligned with Otsuka’s goals, contributing expertise in biomarkers—including omics and digital health technologies (DHT)—as well as clinical and real-world data (RWD). It requires strong computational data analysis, artificial intelligence, and mathematical modeling capabilities, with an emphasis on machine learning and AI methods. The role may also assist in overseeing data or AI scientists, managing resources, providing training, advising cross-functional teams on AI and ML uses, limitations, and impacts, and collaborating with internal and external partners to deliver high-quality data and analytic products. It supports data and analysis strategies across drug development, from discovery and preclinical through clinical stages, and helps evaluate, develop, and integrate emerging AI technologies into R&D workflows.

Requirements

At least 5 years of experience in AI and advanced data sciences within pharmaceutical or biotech R&D industries.
Expertise in scientific data analyses in drug development and real-world data assets, using them to explore biomarkers, endpoints, and scientific or value evidence.
Proficiency in AI, generative AI, and machine learning techniques and relevant tools, such as PyTorch, TensorFlow, and large language models.
Expertise in digital health technologies and their applications in pharmaceutical research.
Strong project management skills focused on delivering impactful initiatives.

Responsibilities

Implement Data Science and AI portfolio objectives and contribute to data and analysis strategies across discovery, preclinical, and clinical stages of drug development.
Identify and apply innovative analytical solutions, including machine learning, tokenization, and digital health technologies.
Develop and apply advanced analyses of biological data, including digital and sensor data, metabolomics, proteomics, and genomics, to support drug development and decision-making.
Investigate and conduct proof-of-concept initiatives and develop AI applications using technologies such as large language models and generative AI to enhance analytics and advance drug candidates.
Assess the feasibility and potential impact of integrating generative AI, large language models, and other small AI models into existing workflows, and recommend adoption strategies based on proof-of-concept outcomes.
Work with cross-functional teams to conduct scientific data analyses and apply AI and generative AI to drug development and evidence generation across the drug lifecycle.
Promote awareness and effective integration of generative AI and AI/ML applications into R&D processes.
Develop processes to optimize data and analytics systems and their execution in alignment with technological advancements.
Guide scientific and technical evaluation, selection, and analysis planning for transitional and digital biomarkers and clinical-trial data.
Lead the planning, development, and implementation of data science solutions using novel AI methodologies.
Guide team members on AI and other methodologies and foster innovation and collaboration.
Collaborate with internal and external stakeholders to identify opportunities for innovation and experimentation with emerging data science and analytics technologies.
Serve as a subject matter expert, supporting awareness, communication, and collaboration for portfolio success.
Monitor industry trends and AI/ML advancements and provide insights to inform the organization’s technical roadmap and strategy.
Build an internal and external presence through publications in relevant conferences and journals.
May assist with overseeing a team of data or AI scientists, managing resources, providing training, advising cross-functional teams, and collaborating with partners to deliver data and analytic products.