.At AstraZeneca, we work together to deliver innovative medicines to patients across global boundaries. We make an impact and find solutions to challenges. We do this with integrity, even in the most difficult situations, because we are committed to doing the right thing. The Digital Health Oncology R&D; Human-centered AI and Machine Learning Team strives to transform the patient experience and clinical trial process. We will do so by deploying digital solutions to clinical trials and in the real world to decrease patient burden. The approach the team takes will incorporate clinical trial data, Real World Evidence (RWE) data, clinical free text, medical imaging, Patient Reported Outcomes (PROs), and device data to define new digital approaches to addressing the pressing problems across the AZ R&D; portfolio. The team is looking for an Associate Director of Data Science to specialize in the development of innovative machine learning methods focused on multi-modal datasets including clinical trial data, RWE, imaging data, and other biomedical data sources to address patient burden. Responsibilities: Drives projects with hands-on data science, analysis, or mathematical modeling. Delivers sophisticated data science solutions to AstraZeneca projects, selecting analytical methods with appropriate complexity given a clinical or business challenge. Engages technical and non-technical collaborators within the wider organization to frame complex challenges, structure analytical solutions, and communicate results. Independently keeps own knowledge up to date and learns from senior team members, proposing appropriate training courses for personal development. Reviews working practices and ensures non-compliant processes are raised. Collaborates in a multidisciplinary environment with world-leading clinicians, data scientists, biological experts, statisticians, and IT professionals. Essential Qualifications: B.Sc. in a relevant field (such as mathematics, computer science, engineering). Demonstrated an outstanding track record of industry experience with the desired data science methodologies. Practical software development skills in standard data science tools: Python, Agile, code versioning (bitbucket/git), UNIX skills, familiarity working in a cloud environment (AWS preferred). End-to-end experience leading collaborative data science projects in an industry setting. ML Ops experience: model tracking, model governance, multiple models in different production contexts. Experience developing machine learning first products including time-series analysis, forecasting, behavioral analysis. Knowledge of a range of mathematical and statistical modeling techniques and drive to continue to learn and develop these skills. Communication, business analysis, and consultancy; ability to present compelling cases to stakeholders and operate dynamically to identify solutions. Residence: Barcelona