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- Tasks: Lead innovative projects in drug development using graph-based machine learning techniques.
- Company: Join AstraZeneca's PT&D team, transforming science into life-saving medicines.
- Benefits: Work in a dynamic environment with opportunities for professional growth and impactful contributions.
- Why this job: Be at the forefront of medical innovation, shaping the future of healthcare.
- Qualifications: Advanced degree in relevant fields and strong coding skills in Python or R required.
- Other info: No telecommuting; EU work permit needed.
The predicted salary is between 43200 - 72000 £ per year.
As an Associate Principal Graph Data Scientist, you'll leverage your expertise to lead groundbreaking projects that revolutionise our drug development processes. Working within the PT&D department, you will be instrumental in transforming molecules into innovative medical treatments. PT&D is at the forefront of developing breakthrough synthetic routes, drug formulations and delivery technologies to ensure our products meet the highest standards of efficacy, safety, and quality.
In this role, you will lead projects involving chemical reaction modelling, synthesis pathway optimization, chemical property prediction, and scientific knowledge discovery using graph-based machine learning techniques. Your contributions will be vital in shaping our approach to drug development and advancing our mission to deliver life-changing medicines to patients.
Requirements:
- Advanced degree in computer science, data science, artificial intelligence, machine learning or related fields.
- Excellent coding skills in languages such as Python, R.
- Significant industrial experience in data science with a focus on graph machine learning and experience with ML frameworks like PyTorch, TensorFlow, or DGL.
- Hands-on industrial experience with extracting insight from graph databases such as Neo4j Enterprise.
- Significant hands-on industrial experience with applied machine learning domains such as deep learning, NLP, GenAI.
- Experience developing data science models and partnering with MLOps teams to productionise models.
About the Company:
Pharmaceutical Technology and Development (PT&D) is the organisation at AstraZeneca that turns brilliant science into actual medicines that help millions of people. We work across the entire value chain, designing and delivering active ingredients, formulations and devices for new medicines and providing expert technical support to all AstraZeneca’s commercial drug substances and products to ensure we successfully supply medicines to patients.
Associate Principal Graph Data Scientist employer: Python Software Foundation
Contact Detail:
Python Software Foundation Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Associate Principal Graph Data Scientist
✨Tip Number 1
Familiarise yourself with the latest advancements in graph machine learning and related technologies. Being well-versed in current trends will not only boost your confidence during discussions but also demonstrate your genuine interest in the field.
✨Tip Number 2
Network with professionals in the pharmaceutical and data science sectors. Attend relevant conferences, webinars, or meetups to connect with industry experts and learn about potential opportunities at AstraZeneca.
✨Tip Number 3
Prepare to discuss specific projects where you've applied graph-based machine learning techniques. Be ready to explain your thought process, challenges faced, and how you overcame them, as this will showcase your practical experience.
✨Tip Number 4
Research AstraZeneca's recent projects and initiatives in drug development. Understanding their goals and challenges will help you tailor your conversations and show how your skills can contribute to their mission.
We think you need these skills to ace Associate Principal Graph Data Scientist
Some tips for your application 🫡
Understand the Role: Before applying, make sure you fully understand the responsibilities and requirements of the Associate Principal Graph Data Scientist position. Familiarise yourself with the specific skills needed, such as expertise in graph machine learning and experience with Python and R.
Tailor Your CV: Customise your CV to highlight relevant experience in data science, particularly in graph-based techniques and machine learning frameworks like PyTorch or TensorFlow. Emphasise any hands-on experience with graph databases such as Neo4j.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for drug development and how your skills align with the company's mission. Mention specific projects or experiences that demonstrate your ability to lead innovative data science initiatives.
Proofread and Edit: Before submitting your application, carefully proofread all documents for spelling and grammatical errors. Ensure that your application is clear, concise, and free of jargon that may not be understood by all reviewers.
How to prepare for a job interview at Python Software Foundation
✨Showcase Your Technical Skills
Be prepared to discuss your coding skills in Python and R, as well as your experience with graph machine learning. Bring examples of past projects where you successfully applied these skills, especially those involving frameworks like PyTorch or TensorFlow.
✨Demonstrate Your Problem-Solving Abilities
Expect to face technical questions that assess your problem-solving capabilities. Prepare to explain how you've tackled complex data science challenges, particularly in the context of drug development or chemical property prediction.
✨Familiarise Yourself with Graph Databases
Since the role involves working with graph databases like Neo4j, make sure you understand their functionalities and can discuss how you've used them in previous roles. Highlight any specific projects where you extracted insights from such databases.
✨Align with the Company's Mission
Research AstraZeneca's mission and values, particularly in the context of drug development. Be ready to articulate how your background and aspirations align with their goal of delivering life-changing medicines to patients.
