AstraZeneca operates in over 100 countries and its innovative medicines are used by millions of patients worldwide.
Machine Learning for Chemical Synthesis
Do you have expertise in, and passion for, Chemistry and Machine Learning? Would you like to apply your expertise to impact the digital transformation strategy in a company that follows the science and turns ideas into life changing medicines? Then you might be the one we are looking for!
Pharmaceutical Sciences at AstraZeneca is developing a digital transformation strategy to drive innovation, optimization of the drug development process and data-driven decision making.We are now recruiting two highly skilled and motivated scientists to specialise in the application of artificial intelligence (AI) and machine learning (ML) in the Early Chemistry Development (ECD) function within the Pharmaceutical Sciences function. One role will focus on synthesis planning and one role will be a data scientist in process design.
At AstraZeneca we turn ideas into life changing medicines. Working here means being entrepreneurial, thinking big and working together to make the impossible a reality. The Pharmaceutical Sciences’ mission is to transform molecules into investigational medicines, meeting the unmet needs of future patients. Our portfolio of projects covers all AstraZeneca’s therapy areas, spanning traditional small molecules, proteins, oligonucleotides and RNA based therapeutics. The ECD group is a multidisciplinary function comprising chemistry, analytical, biopharmaceutics, solid state and predictive sciences. We provide input into compound design & selection, as well as API route and process design from discovery up to and including Phase II clinical studies.
As a Senior Scientist/Associate Principal Scientist within the Computational Pharmaceutics team, you will lead scientific development in one of following areas.
- Synthesis planning: Developing synthesis planning approaches for route selection and process design using AI & ML approaches. This role is focused on reaction optimization and route design to support drug development. The role will exploit internal and external databases to develop route selection workflows optimized for AstraZeneca chemists. The role is placed either in Macclesfield (UK) or in Gothenburg (Sweden).
- Data scientist for process design: Working with the Pharmaceutical sciences community to adopt a data-first culture, recognizing opportunities to utilise AI & ML to generate knowledge and innovation and work alongside scientific experts to design projects to optimize underlying data structure. The role will focus on data extraction, analysis and capture strategies to support data-driven decision making. As the lead Data Scientist in ECD you will maintain awareness of state -of-the art applications of AI and engage with AZ leadership to design and influence strategic decisions. The role is placed Gothenburg (Sweden).
Major Duties and Responsibilities
In these roles, you will work across the Global Pharmaceutical Sciences departments along with other senior scientists to provide scientific leadership and drive innovation. You will be responsible for engaging with key internal and external stakeholders to identify and validate impactful solutions, through the use of machine leaning, data analysis and statistical techniques. You will be at the forefront of the digital transformation within AstraZeneca and drive the development of the digital transformation Pharmaceutical Sciences strategy. Specific responsibilities include:
- You will work in chemical development projects, and platform capability teams to identify opportunities to deliver solutions using machine learning, advanced data mining and predictive modelling.
- Develop novel algorithms, techniques and curate datasets to answer relevant drug development challenges.
- Impact chemical development projects by efficiently and pro-actively applying machine-learning to route and process design challenges.
- Establishing academic collaborations to access and drive the development of machine learning to process chemistry.
- Represent Pharmaceutical Sciences modelling and simulation externally to drive scientific excellence and AstraZeneca’s reputation in the field.
- The candidate will be expected to plan, write and publish high quality scientific papers.
- Act as a role model and mentor to scientists with less experience in machine learning and champion FAIR data practices.
- Develop training material and provide training to AstraZeneca user groups.
- PhD in a relevant area e.g. machine learning, computational chemistry, computer science, data science, mathematics or similar and typically with post-doctoral experience.
- Programming proficiency and experience with relevant software tools such as Python, R or similar), version control (Git/Bitbucket) and database tools.
- Excellent awareness of recent literature within field of expertise and relevant publication record.
- Good track record of professional achievement and recognition in own field.
- Understanding of pharmaceutical R&D and the wider business.
- Able to think beyond boundaries of own job, challenging status quo and seeking opportunities for business improvement.
- Capable of making effective decisions, acting courageously and communicating with conviction and inspiration at all levels.
- Excellent team working, networking, communication and influencing skills.
Specific technical requirements
- Synthesis Planning: (i) Detailed knowledge of a chemoinformatic toolkits, (ii) University level of chemistry will be a minimum requirement.
- Data scientist for process Design: (i) Broad experience in data science, statistics, data models/structures, machine learning and AI. (ii) A foundation level knowledge of organic chemistry is desirable.
If you are interested, apply now!
For more information please contact recruiting manager Anders Broo, PhD on +46 722 108177
Welcome with your application no later than April 24, 2019
AstraZeneca is an equal opportunity employer. AstraZeneca will consider all qualified applicants for employment without discrimination on grounds of disability, sex or sexual orientation, pregnancy or maternity leave status, race or national or ethnic origin, age, religion or belief, gender identity or re-assignment, marital or civil partnership status, protected veteran status (if applicable) or any other characteristic protected by law.