Associate Principal AI Scientist

at  AstraZeneca

ESSENTIAL SKILLS/EXPERIENCE:

• A PhD in computer science, statistics, mathematics, physics, biology or related field OR MSc and proven experience developing artificial intelligence and machine learning models.
• Experience with deep learning methods and their applications, particularly to problems in biology and chemistry.
• Knowledge of computational biology and demonstrated experience in incorporating it into machine learning models.
• Understanding of the AI/ML lifecycle, including data handling, feature engineering, model training, and optimisation.
• Ability to exploit the simplest tricks to the latest research methods to advance AI/ML capabilities while implementing them in an elegant, stable, and scalable way.

DESIRABLE SKILLS/EXPERIENCE:

• Research experience demonstrated by journal and conference publications in prestigious venues (with at least one publication as a leading author). Examples include but are not limited to NeurIPS, ICML, ICLR and JMLR.
• A track record of successfully collaborating with AI engineering teams to deliver complex machine learning models and production-ready data and analytics products.
• Demonstrated experience incorporating them into artificial intelligence approaches and machine learning models.
• Fluent in Python, R, and/or Julia other programming languages, including scientific packages and libraries (e.g. PyTorch, TensorFlow, Pandas, Numpy, Matplotlib).
• Practical ability to work on cloud computing environments like AWS, GCP, and Azure.
• Experience in end-to-end research, development and implementation of artificial intelligence and machine learning solutions that apply existing algorithmic, mathematical, computational, and statistical approaches and might advance the state-of-the-art, with a practical, in-depth understanding of suitable approaches for a particular problem.
• In-depth knowledge of one or more of the following areas such as causal inference, machine learning, deep learning, control theory, natural language processing, multi-scale modelling, reinforcement learning, mathematical optimisation and simulation, signal processing, game theory, statistical inference, operations research, pattern recognition, recommendation systems, probabilistic programming and/or related areas.
• Domain knowledge of tools, techniques, methods, software, and approaches in one or more areas, such as protein engineering, microbiology, structural biology, molecular design, biochemistry, genomics, genetics, bioinformatics, molecular, cellular and tissue biology.
• Evidence of open-source projects, patents, personal portfolios, products, peer-reviewed publications, or 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 and 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 and ambitious world.

Please refer the Job description for details