Typical Accountabilities:
• Coordinate the implementation of novel modelling solutions designed to drive the interrogation of datasets for insights in scientific and business application areas within defined project scope. This includes integrating complex data from multiple different sources and modalities includes the application of specialized approaches in classification, regression, clustering, NLP, image analysis, graph theory and/or other techniques.

• Using domain-specific understanding, translates unstructured, complex business problems into the appropriate data problem, model and analytical solutions

• Researches and develops advanced predictive models and computational methods to guide and shape decision-making within the project scope.

• Provide training and advice to collaborators on optimal use of key data, analysis platforms and the appropriate use of data science.

• Apply expert AI research techniques, including establishment of hypotheses that can be approached using computational methods and tools. Present or publish findings for conferences and in peer reviewed journals.

• Build and manage effective relationships with stakeholders to ensure utilization and value of information resources and services. Clearly and objectively communicate results, as well as their associated uncertainties and limitations to shape solutions

• Provide advanced data science expertise to cross-functional projects and shape delivery of data science solutions that drive value to AstraZeneca

• Apply a range of data science methodologies, developing novel data science solutions where off-the-shelf methodologies do not fit

• Develop, implement and maintain required tools and algorithms in a manner which meets regulatory and evidential requirements within project scope

• Leads small (2-3 person) data science projects of defined scope and provide coaching for junior team members

• Developing, maintaining and applying ongoing knowledge and awareness in trends, standard methodology and new developments in analytics and data science

• Review and develop working practices to ensure that data science work is delivered to robust quality standards


Typical People Management Responsibility (direct / indirect reports):
• Approximate number of people managed in total (all levels) - 2-3
• Manager of a team
• Matrix Manager – (projects/dotted line)

What is the global remit? (how many countries will the role operate in?):
• Another country

Education, Qualifications, Skills and Experience:
• Essential: Masters degree in mathematics, computer science, engineering, physics, statistics, economics, computational sciences, or a related quantitative discipline; or equivalent experience; Demonstrated experience with modern data science approaches, including unsupervised and supervised classification and regression algorithms such as k-means clustering, support vector machines, random forests, neural networks and deep learning. May also have expertise in advanced statistical modelling, or broader aspects of applied mathematics such as dynamical systems or optimisation.; Proven demonstrated experience in the modelling of complex datasets in applied business and/or scientific application domains; Advanced software development skills in at least one of the standard data science languages (such as R, Julia or Python) and familiarity with database systems (e.g. SQL, NoSQL, graph); Experience of manipulating and analysing large high dimensionality unstructured datasets, drawing conclusions, defining recommended actions, and reporting results across stakeholders; Understanding of algorithm design, development, optimization, scaling and applications; Excellent written and verbal communication, business analysis, and consultancy skills; Good understanding of at least one business area where the data science is applied
• Desirable: PhD degree in mathematics, computer science, engineering, physics, statistics, economics, or a related quantitative discipline.; Comfortable working in high performance computing or cloud environment; Proven track record of publishing relevant predictive modelling results and tools in peer-reviewed journals, conferences, and other scientific proceedings.; Experience in life sciences and healthcare; Experience in novel methods development and application

Key Relationship to reach solutions:
• Internal (to AZ or team): Project and program managers; Key business and scientific collaborators; Mid to senior level managers in scoping and prioritizing opportunities
• External (to AZ): Support services from key vendors; Peers across professional groups; Academic collaborators in relevant areas

Date Posted

Closing Date

AstraZeneca embraces diversity and equality of opportunity.  We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills.  We believe that the more inclusive we are, the better our work will be.  We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics.  We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.