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- Tasks: Lead the development of data pipelines and IT solutions for clinical trials.
- Company: Join Novartis, a leader in innovative medicines and healthcare solutions.
- Benefits: Enjoy a supportive work environment with opportunities for personal and professional growth.
- Why this job: Be part of a dynamic team driving scientific advancements through AI and data engineering.
- Qualifications: MSc or PhD in relevant fields with 4-6 years of experience in data engineering.
- Other info: Embrace a culture of continuous learning and collaboration in a diverse workplace.
The predicted salary is between 43200 - 72000 £ per year.
Associate Director, Data Engineering Lead
Job ID: REQ-10026406
Oct 24, 2024
Location: United Kingdom (London, UK; Dublin, Ireland)
Summary
Complex data are integral to our work in clinical studies. This position is part of a newly established AI & Data Engineering team within the Advanced Quantitative Sciences function at Novartis. Our mission is to leverage automation and artificial intelligence to unlock the potential of complex scientific data sources. By accelerating quantitative decision-making in clinical trials through high-quality data assets and tools, we aim to bring innovative medicines to patients faster.
The Data Engineering Lead guides and drives execution of the development of robust data assets from both internal and external sources to support data interrogation, driving research and development efforts, and external collaborations. By collaborating with our quantitative science community, TSC and Statistical Programming colleagues, IT, QA, BR data science teams, and vendors, the Data Engineering Lead ensures the delivery of fit-for-purpose and high-quality data assets, automation pipelines, and associated technical and quality documentation, facilitating scientific advancements in drug development.
Key Responsibilities
- Develop data pipelines and IT infrastructure solutions to enable Quantitative Sciences to utilize high-quality datasets for quantitative decisions at trial and/or project level activities.
- Provide technical leadership for data engineering projects, plan and oversee projects from conception to deployment, define project scope, goals, and deliverables, monitor project progress, and adjust as necessary to meet deadlines.
- Build strong collaborative working relationships and communicate effectively with Quantitative Science partners and clinical teams.
- Play a lead role in agile engineering and consulting, providing guidance on complex data and unplanned data challenges.
- Focus on risk, quality & compliance, proposing and implementing improvements to existing processes, ensuring all data engineering processes are well-documented, and ensuring compliance with legal and regulatory requirements.
- Stay updated with industry trends and advancements, and help establish and strengthen the link between Novartis and the external data engineering community.
- Encourage a culture of continuous learning, constructive collaboration, and innovation within the team.
What You Will Bring to the Role
- MSc or PhD in Computer Science/Engineering, Data Sciences, Bioinformatics, Biostatistics, or any other computational quantitative science.
- Minimum of 4-6 years of developing data pipelines & data infrastructure, ideally within a drug development or life sciences context.
- Expert in software/data engineering practices including versioning, release management, deployment of datasets, agile & related software tools.
- Strong software development skills in R and Python, SQL.
- Strong working knowledge of at least one large-scale data processing technology (e.g. High-performance computing, distributed computing).
- Strong interpersonal and communication skills (verbal and written) effectively bridging scientific and business needs.
- Proven record of delivering high-quality results in quantitative sciences and/or a solid publication track record.
- Experience in Artificial Intelligence (AI), Big Data, Data Governance, Data Management, Data Quality, Data Science, Data Strategy, Data Visualization, Master Data Management.
- Experience in Machine Learning (ML), Python and R, Statistical Analysis.
Benefits and Rewards
Read our handbook to learn about all the ways we’ll help you thrive personally and professionally.
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, please send an e-mail to and let us know the nature of your request and your contact information.
Why Novartis
Helping people with disease and their families takes more than innovative science. It takes a community of smart, passionate people like you. Ready to create a brighter future together?
Join Our Novartis Network
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GB16 (FCRS = GB016) Novartis Pharmaceuticals UK Ltd.
Functional Area: Data and Digital
Job Type: Full time
Employment Type: Regular
Shift Work: No
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Associate Director, Data Engineering Lead employer: Advanced Accelerator Applications (Italy) - S...
Contact Detail:
Advanced Accelerator Applications (Italy) - S... Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Associate Director, Data Engineering Lead
✨Tip Number 1
Familiarize yourself with the latest trends in data engineering and AI, especially as they relate to clinical trials. This knowledge will not only help you in interviews but also demonstrate your commitment to staying updated in a rapidly evolving field.
✨Tip Number 2
Network with professionals in the data engineering and life sciences sectors. Attend industry conferences or webinars where you can meet potential colleagues and learn more about the challenges they face, which can give you insights to discuss during your application process.
✨Tip Number 3
Showcase your experience with agile methodologies and collaborative projects. Be prepared to discuss specific examples of how you've led teams or contributed to successful data engineering projects, as this aligns closely with the responsibilities of the role.
✨Tip Number 4
Highlight your technical skills in R, Python, and SQL during conversations. Being able to articulate your proficiency in these languages and how you've applied them in real-world scenarios will set you apart from other candidates.
We think you need these skills to ace Associate Director, Data Engineering Lead
Some tips for your application 🫡
Understand the Role: Before applying, make sure you fully understand the responsibilities and requirements of the Associate Director, Data Engineering Lead position. Tailor your application to highlight relevant experiences that align with the job description.
Highlight Relevant Experience: In your CV and cover letter, emphasize your experience in developing data pipelines and IT infrastructure, particularly in drug development or life sciences. Use specific examples to demonstrate your expertise in software/data engineering practices.
Showcase Technical Skills: Clearly outline your technical skills in R, Python, SQL, and any large-scale data processing technologies you are familiar with. Mention any experience with AI, Big Data, and Machine Learning, as these are crucial for the role.
Communicate Effectively: Since strong interpersonal and communication skills are essential for this position, ensure your application reflects your ability to bridge scientific and business needs. Use clear and concise language to convey your ideas and experiences.
How to prepare for a job interview at Advanced Accelerator Applications (Italy) - S...
✨Showcase Your Technical Expertise
Be prepared to discuss your experience with data pipelines and infrastructure. Highlight specific projects where you utilized R, Python, or SQL, and explain how you overcame challenges in those projects.
✨Demonstrate Leadership Skills
As a Data Engineering Lead, you'll need to show your ability to guide teams and manage projects. Share examples of how you've led teams through complex data challenges and ensured project deliverables were met on time.
✨Emphasize Collaboration
This role requires strong collaboration with various teams. Be ready to discuss how you've built relationships with quantitative science partners and clinical teams, and how effective communication has played a role in your success.
✨Stay Updated on Industry Trends
Show your passion for continuous learning by discussing recent advancements in AI and data engineering. Mention any relevant conferences, workshops, or publications that have influenced your approach to data management and engineering.
