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TakedaPharmaceutical Nordics AB · 13 hours ago

Director, Generative AI

Boston, MA

Full-time

Onsite

Director/Executive

$174K/yr - $274K/yr

8+ years exp

Takeda Pharmaceutical Nordics AB is inviting individuals with deep knowledge of machine learning and artificial intelligence to join their ShinrAI Center for AI/ML in Cambridge, MA. The role involves partnering with various teams to leverage generative AI in pharmaceutical R&D, translating business needs into machine learning projects, and ensuring ethical application of AI/ML technologies.

Pharmaceuticals

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

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

Qualification

Generative AIMachine LearningAI/ML SystemsDeep LearningNatural Language ProcessingComputer VisionMLOpsCuriosityCommunication SkillsInterpersonal SkillsAttention to Detail

Required

Deep knowledge of machine learning and artificial intelligence with extensive experience in Generative AI

Strong curiosity for a deeper understanding of human health and disease to deliver innovative medicine for patients

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

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

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 models in applied settings

Familiarity with ML Ops concepts related to testing, retraining, and monitoring models in production

Experience in delivering custom software solutions for complex R&D needs, leveraging both internal and external resources

Expertise in implementing DevOps practices to drive efficient development-to-operations transitions and ensure automation across workflows

Experience in configuring and working in different coding environments (local, notebooks, containers) and using standard software engineering workflows (testing, code management/Git, CI/CD)

An enthusiasm to ask questions and try and learn new things