Associate Principal Graph Data Scientist – Pharmaceutical Development
| Posting date: | 29 May 2025 |
|---|---|
| Hours: | Full time |
| Closing date: | 05 June 2025 |
| Location: | Macclesfield, Cheshire |
| Remote working: | On-site only |
| Company: | AstraZeneca |
| Job type: | Permanent |
| Job reference: |
Summary
As an Associate Principal Graph Data Scientist, you'll leverage your expertise to lead groundbreaking projects that revolutionize 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.
The position will be based at our vibrant site in Gothenburg (Sweden) or Macclesfield (UK).
Accountabilities
Use graph theory to extract meaningful scientific patterns, community structures, and informative insights from large graph datasets.
Develop methodologies for computational drug development using graph-based machine learning techniques.
Create visualizations to aid in the intuitive representation of graph data and to facilitate stakeholder engagement and interpretation of results.
Collaborate with cross-functional teams ensuring knowledge transfer to IT engineering teams for IT solution builds and deployment.
Keep pace with industry advancements by reviewing academic papers and attending conferences. Publish findings in peer-reviewed journals and represent the company at scientific forums.
Communicate technical concepts and results to technical and non-technical audiences.
Essential skills/experience:
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
Desirable skills/experience:
Contributions to open-source projects. If you meet this criteria, please highlight merged GitHub PRs in your application.
Strong publication record in the field of AI.
Experience delivering machine learning projects with applications in pharmaceutical development, chemical engineering or chemistry.
Experience with one or more of the following applied machine learning domains such as transfer learning, federated learning, few/zero shot learning, meta learning, explainable AI.
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.
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.
The position will be based at our vibrant site in Gothenburg (Sweden) or Macclesfield (UK).
Accountabilities
Use graph theory to extract meaningful scientific patterns, community structures, and informative insights from large graph datasets.
Develop methodologies for computational drug development using graph-based machine learning techniques.
Create visualizations to aid in the intuitive representation of graph data and to facilitate stakeholder engagement and interpretation of results.
Collaborate with cross-functional teams ensuring knowledge transfer to IT engineering teams for IT solution builds and deployment.
Keep pace with industry advancements by reviewing academic papers and attending conferences. Publish findings in peer-reviewed journals and represent the company at scientific forums.
Communicate technical concepts and results to technical and non-technical audiences.
Essential skills/experience:
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
Desirable skills/experience:
Contributions to open-source projects. If you meet this criteria, please highlight merged GitHub PRs in your application.
Strong publication record in the field of AI.
Experience delivering machine learning projects with applications in pharmaceutical development, chemical engineering or chemistry.
Experience with one or more of the following applied machine learning domains such as transfer learning, federated learning, few/zero shot learning, meta learning, explainable AI.
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.