Job Description Summary
Global Head of Data Engineering - Advanced Methodology and Data ScienceJob Description
Key Responsibilities:
Promote cutting-edge efficient data engineering and data wrangling for priority pools to support in-depth data interrogation at the project and trial level for priority programs
Be the Analytics technical lead with the data asset owner from Analytics DU team in order to plan, create and maintain data products in an efficient and scalable manner
Support cross-functional teams in utilizing these data assets from Biomedical Research through Biomarker Development, Precision medicine through Development and medical affairs, and work with internal & external data engineering experts to grow capabilities and infrastructure in a reusable and scalable way.
Bridge data and knowledge engineering together with more traditional statistical programming skills, to bring automation and modern processes to reporting activities for trials and programs:
Establish data engineering as a process and function that is an essential capability to leverage complex (multi-modal and/or pooled) datasets to generate evidence that can support HA interactions / submission work and in-depth data interrogation.
Establish data engineering practices that ensure automated testing with documented code-review and have automated, reproducible QC reports accompany all data releases.
Engage as a key contributor to the AMDS (Advanced Methodology and Data Science) Leadership team, propose, lead or contribute to functional or cross-functional data engineering initiatives, coordinating diverse team members to support priority projects across Analytics, Statistical Methods and AEA (Advanced Exploratory Analytics) teams in AMDS.
Attract and develop strong talent to a team that can deploy a range of data and software engineering skills alongside data science and AI in order to support data engineering activities across the broader Analytics group and extend to Biomedical Research.
Bring data and software engineering mindset and deep technical expertise to the technology infrastructure projects, the wider organization and the pharma community:
Essential Requirements:
Deep knowledge and 10+ years of experience in global drug development, including data pooling strategy, technology and computing environments, AI and modernization
Proven expertise in quantitative sciences, including good data science practices, working with and engineering multi-modal large data, strategic input to technology platforms
Experience working in a fast-paced environment with multiple competing priorities
proven ability to operate in a matrixed environment, with a high tolerance for ambiguity and change
Excellent presentation and communication skills, influencing and senior stakeholder management skills and collaboration
Thorough knowledge of GxP, IT systems, QA and regulatory/clinical development process
Ability to represent the organization in senior internal and external forums
Fluency in English, written and oral
Benefits and rewards:
Read our handbook to learn about all the ways we’ll help you thrive personally and professionally:
https://www.novartis.com/careers/benefits-rewards
Commitment to Diversity & Inclusion:
We are committed to building an outstanding, inclusive work environment and diverse teams representative of the patients and communities we serve.
Accessibility and accommodation:
Novartis is committed to working with and providing reasonable accommodation to all individuals. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the recruitment process, or in any order to receive more detailed information about essential functions of a position, please send an e-mail to inclusion.switzerland@novartis.com and let us know the nature of your request and your contact information. Please include the job requisition number in your message.
Skills Desired
Algorithms Development, Applied Mathematics, Artificial Intelligence (AI), Big Data, Culture Change, Data Analytics, Data Governance, Data Literacy, Data Management, Data Quality, Data Science, Data Strategy, High-Performance Computing, Information Architecture, Machine Learning (Ml), Master Data Management, Mentorship