Real World Data Scientist

Job Title: Real World Data Scientist

Introduction to role:
We are seeking a highly skilled and passionate Data Scientist to join ‘Beam’, our cutting-edge healthcare Real-World Data (RWD) research center. This role focuses on leveraging federated learning and advanced analytics to accelerate Real-World Evidence (RWE) for the benefit of patients in Israel and worldwide. As a data scientist, you will be responsible for overseeing our entire data ecosystem, from data strategy development to the successful implementation of data-driven initiatives at a global level. The ideal candidate will collaborate with multidisciplinary teams to develop scalable statistical models while ensuring rigorous data privacy and regulatory standards. This is an exciting opportunity to work at the intersection of data science, healthcare, and emerging technologies!

Accountabilities:
- Develop scalable data research capabilities by designing and implementing federated learning models for decentralized analysis, ensuring high data privacy standards.
- Build predictive models, anomaly detection systems, and other analytics tools tailored to real-world data analytics use cases.
- Apply deep learning and other advanced techniques to analyze large and complex datasets.
- Monitor and optimize research models for accuracy, efficiency, and scalability when required.
- Guide the definition of research questions, design workflows, and establish best practices for statistical and machine learning analyses.
- Conduct data harmonization, statistical analysis, and support clinical and scientific needs assessments.
- Write and review literature, develop study designs, and draft research protocols.
- Analyze large, complex data sets, define, and prioritize data initiatives, ensuring their successful execution, including timely delivery of project milestones, while maintaining high data quality.
- Lead research initiatives and publish findings in relevant journals or conferences.
- Identify trends, patterns, and best practices in data accessibility, management, analytics, and emerging technologies to drive innovation and continuous improvement in data-related processes and methodologies.
- Foster research collaborations across internal and external teams and develop trusted relationships with researchers and global stakeholders to advance data-driven initiatives.
- Train internal teams to adopt new processes and technologies aligned with research objectives.

Essential Skills/Experience:
- MSc in Data Science, Computer Science, Statistics, Mathematics or any other relevant field
- 3+ years of experience in data science, with experience with real-world data analysis.
- Strong programming skills (Python, R) and familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch or Scikit-learn)
- Hands-on experience with healthcare data standards (HL7, FHIR), medical terminologies used in healthcare data (e.g. ICD9) EMRs, and regulations (HIPAA, GDPR).
- Experience in supporting pharmacoepidemiology studies with proven track record of advancing approaches with statistics/machine learning/AI
- Ability to align data initiatives with business objectives and drive data-driven decision-making at all levels of the organization.
- Translate complex business processes into actionable data models to facilitate decision-making.
- Excellent problem-solving skills and a proven ability to work in a collaborative environment.
- Demonstrated ability to build long-term relationships with stakeholders, understand relevant scientific/business challenges and translate into a program of data science activities to deliver value to the business.
- Exceptional organizational skills and strong attention to details.
- Ability to work effectively with different teams and multiple tasks.

Desirable Skills/Experience:
- Ph.D. in related health science field.
- Expertise in federated learning or decentralized analytics.
- 2+ years' experience in the healthcare industry
- Familiarity with cloud platforms and secure data infrastructure
- Ability to incorporate novel data science techniques into projects
- Excellent understanding of overall business strategy and its link to the various disease areas
- Ability to make complex judgments, develop innovative solutions, and apply comprehensive cross-functional and industry understanding to individual projects and programs
- Ability to look for more efficient and effective methods and processes of delivering quality studies while conscious of compliance and governance in the company.
- Excellent verbal and written communication skills, in Hebrew and in English
- Good real-world evidence and clinical studies understanding and ability to learn relevant therapeutic and disease area knowledge
- Ability to work effectively with different teams and multiple tasks
- Team-oriented and agility approach; ability to respond quickly to shifting demands and opportunities.
- Highly motivated with strong self-learning ability

At AstraZeneca, we are bold in setting the direction for a new future where patients live longer and enjoy better quality of life. We combine new medicines with unique healthcare solutions across the whole patient journey. Our team is united by a local sense of belonging while being part of a global network. We embrace digital transformation to drive innovation. Here, you will find opportunities to learn from the best global talent in a culture that fosters collaboration. We are committed to making a positive impact on people’s lives through our transformative products.

Ready to make a difference? Apply now!

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.