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Principal Data Scientist

Location Gaithersburg, Maryland, United States Cambridge, England, United Kingdom Gothenburg, Västra Götaland County, Sweden Job ID R-071094 Date posted 01/14/2020

Principal Data Scientist

The AI and Analytics team within AstraZeneca’s R&D Data Science and AI group uses sophisticated algorithms and techniques to solve some of the hardest problems in the discovery and development of new medicines. The team uses a blend of scientific, problem solving, and quantitative skills to develop and deliver ground breaking methods addressing critical problems in our R&D environment. Our team of data scientists work right next to our other scientists and operational teams, allowing them to be close to the questions that matter and work on a broad range of the most promising opportunities quickly.

The Data Science & AI team collaborates across R&D to drive innovation through data science and AI. Together we seek to:

  • Improve our understanding of disease and uncovering new targets
  • Transform R&D processes
  • Speed the design and delivery of new medicines for patients

Do you want to join us and apply machine learning to tackle difficult problems in drug development? As a Principal Data Scientist, you can play a pivotal role in a rapidly growing team analysing and manipulating various types of biomedical datasets and generating the insights from our complex data that brings innovative medicines to patients faster.


In this role you will join a growing team analysing and manipulating various types of biomedical datasets and generating the insights from our complex data that brings innovative medicines to patients faster.  You will apply your expertise in machine learning, quantitative data analysis and artificial intelligence to develop innovative data science solutions in clinical drug development. To achieve this, you will apply and develop new technologies/methods and work in a highly multidisciplinary environment with world leading clinicians, data scientists, biological experts, statisticians and IT professionals.


We work on a flexible and varied portfolio of challenges that could include – but are not limited to:

  • Researching and developing machine learning models on multi-modal data to improve processes and provide crucial insights to decision-makers,
  • Researching and developing forecasting and optimization methods to guide decision-making about and resourcing of our drug projects
  • Understanding the patient journey in real world data to optimize patient treatment pathways
  • Working with drug project teams to design better drug trials, optimize their supply chains and improve trial participants’ experiences.

These challenges will require you to:

  • Provide advanced data science expertise and deliver 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
  • Translate unstructured, complex business problems into the appropriate data problem, model and analytical solutions
  • Develop, implement and maintain tools and algorithms in a manner which meets regulatory and evidential requirements
  • Clearly and objectively communicate results, as well as their associated uncertainties and limitations
  • Constructively collaborate with a diverse set of users (global functions and local marketing companies) and partners
  • Developing, maintaining and applying ongoing knowledge and awareness in trends, standard methodology and new developments in analytics and data science
  • Mentor and support the data scientists across multiple projects to drive the development of data science as an AstraZeneca capability
  • Review and develop working practices to ensure that data science work is delivered to robust quality standards



  • MSc degree in rigorous quantitative science (such as mathematics, computer science, engineering)
  • Computing skills: scientific computing and scripting (R, Python, Perl, Jupyter, Hadoop, AWS), software development, database design and coding
  • Experience of manipulating and analysing large high dimensionality unstructured datasets, drawing conclusions, defining recommended actions, and reporting results across stakeholders
  • Excellent written and verbal communication, business analysis, and consultancy skills
  • Understanding of algorithm design, development, optimization, scaling and application
  • Customer focus - dedicated to meeting the expectations and requirements of internal and external customers
  • Integrity and trust -- unwavering commitment to "doing the right thing"
  • A passion to apply machine learning to solve difficult problems in drug development


  • PhD degree in rigorous quantitative science (such as mathematics, computer science, engineering)
  • Experience in novel methods development and application
  • Understanding of life sciences, with a preference for clinical drug development and the pharmaceutical environment
  • Experience of end-to-end software tool/solution development and lifecycle management ideally including the RFI/RFP process, and experience with SCRUM/agile is desirable

Possible locations for this role are Gaithersburg US, Cambridge UK or Gothenburg Sweden.

Competitive salary and benefits apply.

Posting date: 14th January 2020

Closing date: 14th February 2020


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 authorisation and employment eligibility verification requirements.

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Great culture, great work assignments, supportive management. Rotation opportunity within the company. They value inclusion and diversity.