Associate Principal - AI Research Scientist in Hartford

Are you passionate about creating artificial intelligence and machine learning systems for real-world science applications? Does contributing to preventing, modifying, and even curing some of the world's most complex diseases inspire you? Would you like to work on developing an iterative drug discovery and development process while drawing on methods across various fields, from active learning to optimisation and search? What about advancing our understanding of biology, streamlining research and development processes, and leveraging a variety of data modalities? Do you thrive working in a supportive, inclusive environment where creativity, collaboration across disciplines and lifelong learning towards innovative breakthroughs are encouraged? If yes, this opportunity may be for you.

Join our interdisciplinary Centre for Artificial Intelligence team working on the frontier of AI research for digital biology. Your work will support the next generation of medicines and vaccines at the intersection of AI, biology, and engineering. Your work will contribute to transforming the drug discovery and development value chain as we know it, uncovering novel biological insights, automating processes, streamlining decisions, and improving the overall pipeline across all therapeutic areas at AstraZeneca.

You will work efficiently in a team to lead and deliver projects optimally, researching, developing and using the novel AI theories, methodologies, and algorithms, with engineering best practices and standard processes for various biology, chemistry and clinical applications. You will be part and also lead multifunctional projects to conceive, design, develop and conduct experiments to test hypotheses, validate new approaches, and compare the effectiveness of different AI/ML systems, algorithms, methods and tools for new applications to support the discovery, design, and optimisation of medicines with improved biological activity.

You will lead and contribute to addressing challenges and opportunities in the drug discovery and development value chain processes and provide innovative solutions in fields such as deep learning, representation learning, reinforcement learning, meta-learning, active learning approaches applied to de novo molecule design, protein engineering, in-silico discovery, structural biology, computational biology, translational sciences, biomarker discovery, clinical research, clinical trials and many other areas.

You will lead and develop machine learning models designed explicitly for analysing heterogeneous biological data while collaborating with biology researchers to run algorithmically designed wet lab experiments to inform future experimental directions. You will remain at the forefront of AI/ML research by participating in journal clubs, seminars, mentoring, and personal development initiatives and contributing to publications and academic and industry collaborations.

A PhD in machine learning, statistics, computer science, mathematics, physics, or a related technical discipline with relevant fundamental research experience in artificial intelligence and machine learning or equivalent practical experience. Fundamental AI research experience in conjunction with foundational knowledge and a proven track record in conceptualising, designing, and creating entirely new models, methods, approaches, architectures, and algorithms from scratch. This is essential as off-the-shelf methods and state-of-the-art AI/ML techniques often do not work on our scientific problems and datasets.

Deep theoretical understanding, combined with a strong quantitative knowledge of algebra, algorithms, probability, calculus, and statistics, as well as extensive hands-on experimentation analysis, and AI/ML techniques visualisation. Well-rounded experience designing new AI/ML approaches to deriving insights from proprietary and external datasets to generate testable hypotheses using algorithmic, mathematical, computational, and statistical methods combined with theoretical, empirical or experimental research sciences approaches.

Experience in theoretical, fundamental AI research and practical aspects of AI/ML foundations and model design, such as improving model efficiency, quantisation, conditional computation, reducing bias, or achieving explainability in complex models. In-depth understanding of applying rigorous scientific methodology to (i) identify and create novel ML techniques and the required data to train models, (ii) develop machine learning model's architectures and training algorithms, (iii) analyse and tune experimental results to inform future experimental directions, and (iv) implement and scale training and inference engineering frameworks and (v) validate hypotheses.

Distinctive experience in exploiting the simplest tricks to the latest research methods to advance AI/ML capabilities while implementing them in an elegant, stable, and scalable way. Thorough algorithmic development and programming experience in Python or other programming languages and standard machine learning toolkits, especially deep learning (e.g., Pytorch, TensorFlow, etc.). Robust ability to communicate and collaborate effectively with diverse individuals and functions, reporting and presenting research findings and developments clearly and efficiently to other scientists, engineers and domain experts from different disciplines.

Fundamental research, extensive research and expert understanding combined with hands-on practical experience and theoretical knowledge of at least two or more of the following research areas - examples include but are not limited to - multi-agent systems, logic, causal inference, Bayesian optimisation, experimental design, deep learning, reinforcement learning, non-convex optimisation, Bayesian non-parametric, natural language processing, approximate inference, control theory, meta-learning, category theory, statistical mechanics, information theory, knowledge representation, unsupervised, supervised, semi-supervised learning, computational complexity, search and optimisation, artificial neural networks, multi-scale modelling, transfer learning, mathematical optimisation and simulation, planning and control modelling, time series foundation models, federated learning, game theory, statistical inference, pattern recognition, large language models, probability theory, probabilistic programming, Bayesian statistics, applied mathematics, multimodality, computational linguistics, representation learning, foundations of generative modelling, computational geometry, geometric methods, multi-modal deep learning, information retrieval related areas.

Fluent in Python, R, Julia and other programming languages including scientific packages libraries (e.g. PyTorch, TensorFlow, Pandas, NumPy, Matplotlib). Experience in machine learning research developing fundamental algorithms frameworks that can be applied to various machine learning problems particularly in biology, chemistry, clinical applications demonstrated track record solving biological issues relevant drug discovery development. Research experience demonstrated by journal conference publications prestigious venues (with at least one publication as leading author). Examples include but are not limited NeurIPS, ICML, ICLR, JMLR.

A track record successfully collaborating AI engineering teams deliver complex machine learning models production-ready data analytics products. Practical ability work cloud computing environments like AWS, GCP, Azure. Domain knowledge tools techniques methods software approaches more areas such protein engineering, microbiology, structural biology, molecular design, biochemistry, genomics, genetics, bioinformatics, molecular cellular tissue biology. Evidence open-source projects, patents, personal portfolios, products, peer-reviewed publications similar track records.

When we put unexpected teams in the same room we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect work at pace challenge perceptions. That's why we work on average a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique ambitious world. AstraZeneca is where curiosity meets courage! We are committed to making a difference by fusing data technology with scientific innovations to achieve breakthroughs that redefine what's possible. Our inclusive environment encourages collaboration across academia, biotechs, industry leveraging diverse global knowledge for swift impact on disease. With opportunities for lifelong learning exploration this is a place where your passion for science can truly make a difference.