Takeda is looking to solve problems in the pharmaceutical R&D organization by leveraging machine learning and artificial intelligence, particularly generative AI and agentic AI, to drive decision making and automation.
Requirements
- Experience building agentic and LLM based solutions
- Experience in fine tuning large language models for domain specific applications
- Experience in designing transfer learning strategy to enable learning from small datasets
- Demonstrated ability and authoritative knowledge in a variety of AI/ML problems and domains, with depth in at least two (computer vision, natural language processing, geometric deep learning, timeseries, reinforcement learning, multimodal learning, etc.).
- Solid understanding of deep learning model architectures (C/RNN, attention/memory, autoregressive, etc.) and extensions (Transformer, LSTM, Autoencoders, etc.) as well as traditional ML models (k-means, KNN, decision trees, SVM, Bayesian/graphical models, Gaussian process, etc.)
- Experience tuning, validating, optimizing, visualizing, and debugging these models in applied settings
- Familiarity with ML Ops concepts related to testing, retraining, and monitoring models in production
Responsibilities
- Partners with data science teams, domain experts, and business units to identify and prioritize opportunities to leverage machine learning and particularly generative AI and agentic AI to drive decision making and automation across all levels of the R&D organization
- Translate business needs into clearly scoped machine learning projects, and take a hands-on approach to steer solution design and implementation
- Educate, demonstrate, guide, and enable the application of machine learning and particularly generative AI in various pharmaceutical R&D operations and scientific domains
- Identify, monitor, and validate relevant external AI/ML developments, cultivate relationships with external domain experts and partners, and report and present emerging novel developments within the organization to further innovation and shape long-term strategy and governance.
- Proactively build relationships across the company to inform your work and contribute to internal and external collaborations, through involvement in working groups, and the writing of insightful, engaging, and actionable opinion pieces that are easily digestible by internal decision makers and stakeholders.
- Be the leading voice for building common capability and approaches and for adopting best practices
- Work in collaboration with our Ethics and Governance teams to ensure our AI/ML applications are developed ethically and provide broad benefits to our patients and business
Other
- An advanced degree (M.S., PhD.) in mathematics, applied statistics, computer science, machine learning or similar
- 8+ years of experience architecting, building, launching, and maintaining end-to-end ML systems from whiteboard to production at scale
- Excellent communication, prioritization, and interpersonal skills, with a high level of attention to detail
- A track record of partnering cross-functionally with a wide range of stakeholders and cross-functional teams to develop and deploy novel data solutions in production environments
- Entrepreneurial experience is desirable
