Director, Data Science

AstraZeneca-posted about 1 year ago

Full-time • Director

Waltham, MA

Chemical Manufacturing

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The Director, Data Scientist position at AstraZeneca involves leading data science and machine learning projects to provide valuable insights for clinical development and related departments. The role focuses on solving complex challenges in drug development, collaborating with various stakeholders, and contributing to strategic discussions while mentoring junior colleagues.

Lead cross-disciplinary project teams in a complex matrix organization to deliver projects across business areas with competing priorities.
Hands-on use of data science, machine learning, and visualization to address challenges faced in drug development, including support for design and interpretation of clinical trials.
Use data science and AI to deliver insights in clinical projects within pharma.
Identify opportunities, drive outcomes and results, and clearly communicate financial and business benefits to a variety of stakeholders.
Contribute to strategic discussions at a portfolio level.
Mentor and coach junior colleagues.
Lead the team to use good software engineering practices, e.g., GitHub, Jira, Docker, unit testing, DVC, etc.
Propose and drive improvements in software engineering practices.
Ensure work is compliant with clinical development and other appropriate regulations and procedures.
Communicate machine learning, statistical methods, and insights to various domain experts, e.g., clinical teams, data scientists, senior management, statisticians, external collaborators.
Develop collaborations with external academic and commercial enterprises.
Contribute ideas and provide creative input within the data science community at AstraZeneca.
Continue to develop as a data scientist/machine learning expert using assigned personal development time.

Ph.D. in machine learning, data science, computer science, statistics or MSc in machine learning, data science, computer science, statistics with substantial experience applying machine learning and data science technologies in a late-stage pharma setting.
Knowledge of disease/therapy area/clinical domain knowledge within pharma, ideally experience in biopharma.
Track record of delivery excellence, with a fail-fast philosophy, and ability to maintain focus in demanding high-impact situations.
Deep knowledge and understanding of machine learning algorithms, including pros and cons of different approaches.
Understanding of how to create robust and well-tested models, considering data privacy and ethical AI principles.
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.).
Excellent communication skills and team working skills to achieve objectives.
Build project level strategy, provide leadership of junior members through mentorship and coaching skills.

Experience within the pharmaceutical industry and ability to demonstrate understanding of drug development and clinical trial process and data is highly desirable.
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 global teams delivering impactful data science projects and analyses for stakeholders.
Multiple published papers and/or patents, contribution to open source projects.

Inclusive culture that champions diversity and collaboration.
Commitment to lifelong learning, growth, and development.

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