Job Specifications
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Job Description
We are inviting individuals with deep knowledge of machine learning and artificial intelligence with extensive experience in Generative AI to join us in the ShinrAI Center for AI/ML at Takeda, based in Cambridge, MA.
At the ShinrAI Center, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the application of artificial intelligence in developing innovative medicine for patients. We like to solve problems, take initiative, pitch in when needed, and enthusiastic for trying new things. We are looking for more curious thinkers who like to tackle challenging, real-world problems in a rewarding environment where your contributions are valued and have a direct impact.
We're building a diverse team whose skills, experiences, and backgrounds complement one another. Extensive experience working in Pharma or Biotech is optional. A strong curiosity for a deeper understanding of human health and disease to deliver innovative medicine for patients is a must.
ACCOUNTABILITIES:
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
Help talented, driven, enthusiastic AI/ML engineers and data scientists across the company grow professionally
Measure, document, and communicate impacts of the Center’s efforts
EDUCATION, BEHAVIOURAL COMPETENCIES AND SKILLS:
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
Demonstrated passion for making complex technology more accessible and the ability to communicate complex technical topics simply and convincingly to a wide range of audiences
Demonstrated ability in translating big picture business and product ideas into micro use cases and has a strong focus on solving core problems to deliver simple solutions
Experience recognizing and communicating the implications of emerging technologies
Excellent communication, prioritization, and interpersonal skills, with a high level of attention to detail
An advanced degree (M.S., PhD.) in mathematics, applied statistics, computer science, machine learning or similar. With 8+ years of experience architecting, building, launching, and maintaining end-to-end ML systems from whiteboard to production at scale across a range of models and platforms, such as: 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.) and their real-world advantages and drawbacks. Experience tuning, validating, optimizing, visualizing, and debugging these mod