Senior Scientist, Data Scientist  

Key Skills

Bioinformatics, Data Analytics, Data science techniques, Data Visualization, Docker, Github, JIRA, Machine learning techniques, matplotlib, Python Programming, Statistics

Job Description

In this role, you will work directly with our stakeholders across BioPharma R&D (which includes respiratory, immunology, cardiovascular, renal, metabolic, neuroscience and vaccines) to deliver insights from clinical data, and work collaboratively with other teams within Data Science and Advanced Analytics to deliver innovative solutions to problems in pharma. Your work will have a significant impact by accelerating the delivery of drugs to our patients. You will work on challenges such as:supporting inclusion criteria through prognostic disease modelling, leveraging innovative methodologies for interpreting clinical trial data, identification of patient sub-groups, andsupporting regulatory interactions. Your work will support activities across early and late stages of clinical development. It is expected you will present your findings to clinical and senior stakeholders and publish your findings in scientific journals and conferences. You will have the opportunity to collaborate extensively with other teams across the organization, with the goal of delivering products and novel insights to improve patient’s lives.

Examples of projects this team works on include:

• Inform clinical study design through machine learning led selection of subjects who are most likely to respond to treatment (eg: super-responders) or to experience a clinical event (eg: fast progressors)

• Model and simulate treatment response in clinical trial data to rank covariates of clinical response 

• Mining real world data to identify covariates of clinical risk

• Deploy novel data science methods for analyzing clinical trial data to support clinical development and regulatory submissions

• Patient risk models 
• Counter-factual event models
• Biomarker driven efficacy models

Key responsibilities

• Exploratory Data Analysis of clinical data using data science, machine learning and visualisation to support design and interpretation of clinical trials
• Apply machine learning to challenges faced in drug development

• Ensure projects make a tangible difference to business decisions to improve patients' lives
• Working to good software engineering practises, eg: GitHub, Jira, Docker, unit testing, DVC, etc.
• Communicate machine learning, statistical methods and insights to various domain experts, eg: clinical teams, data scientists, senior management, statisticians, external collaborators
• Contribute ideas and provide creative input within the data science community at AstraZeneca
• Work efficiently on small teams cross-disciplinary teams to optimally deliver projects across AZ business areas

• Continue to develop as a data scientist / machine learning expert through personal development 

Essential for the role

• Ph.D. or equivalent experience in machine learning, data science, computer science, statisticsOr MSc in machine learning, data science, computer science, statistics and3+ years' experience as a data scientist / machine learning scientist

• Knowledge and understanding of machine learning algorithms – including pros and cons of different approaches
• Understanding of how to create robust and well tested models.
• Experienced in Python and familiar with modern software engineering practices and the use of modern collaborative tools (familiarity with some or all of the following:- AWS or similar cloud technology, GitHub, Jira, Docker, unit testing, DVC etc.)

Desirable for the role

• Ph.D. or equivalent experience in machine learning, data science, computer science, statistics, life sciences or related field 
• Experience within the pharmaceutical industry and ability to demonstrateunderstanding of drug development and clinical trial process and data (all phases)
• Experienced with clinical data standards,GxP framework, medical terminologies and controlled vocabularies used in healthcare data and ontologies

• Use of Machine Learning to address medical or pharmaceutical problems with patient level data
• Track record of collaborating successfully on cross-disciplinary teams delivering impactful data science projects and analyses for stakeholders.