Join Our Data Science Innovation Fellowship at Novartis

Embark on a transformative journey with our Data Science Innovation Fellowship – a three-year post-doctoral research program designed to revolutionize drug discovery. As a fellow, you'll leverage your computational expertise to impact patients' lives and shape the future of medicine at Novartis. Our collaborative environment will have you working with a dynamic team in Biomedical Research, propelling innovation through advanced data science techniques.

Our program offers unique opportunities to access large-scale, real-world biomedical data seldom available in academia. With the guidance of experienced mentors, you'll benefit from a customized training program, potential mini-sabbaticals in other Novartis departments, and participation in conferences and workshops.

Program Highlights

Biomedical Research at Novartis is home to a vibrant postdoctoral community, offering monthly seminars and an annual Research Day Symposium. This is your chance to lead the way in data science and AI, influencing the future of drug discovery.

Your project will enhance disease target identification and drug discovery methodologies, specifically focusing on improving our understanding of drug-kidney interactions. You'll use your quantitative skills to extract mechanistic insights from preclinical models relevant to human kidney biology and pathophysiology.

Start Date: Winter 2024

Key Responsibilities

As a Data Science Innovation Fellow, you will:

• Advance spatial transcriptomics methods, integrating histology images with transcriptomics analysis at scale.
• Explore novel multimodal and transfer learning approaches for translatable kidney function assessments.
• Utilize a vast knowledgebase of rodent studies to analyze preclinical and patient kidney data.
• Identify drug development opportunities by uncovering relationships in large-scale datasets.
• Collaborate with colleagues in toxicology, pathology, early discovery, and data sciences.
• Promote data science and AI best practices to enhance drug discovery processes.
• Communicate research findings at scientific conferences and through publications.

Role Requirements

To be considered for this role, you should have:

• A PhD in a quantitative field (data science, machine learning, computer science, statistics) or life sciences, preferably with a multidisciplinary background (chemoinformatics, bioinformatics, biomedical engineering, AI/ML in drug discovery or life sciences). PhD students in their final year are also eligible to apply.
• A solid understanding of statistics, machine learning, and deep learning.
• Proficiency in data visualization and exploratory analysis.
• Knowledge of Python and/or R, along with machine learning/deep learning libraries. Experience with generative algorithms and explainable AI is a bonus.
• Experience with reproducible data science tools and practices.
• A strong publication record or other scientific accomplishments (e.g., awards, patents, grants, public code repositories).
• Excellent analytical, communication, presentation, and organizational skills.
• A passion for research and unbounded curiosity.
• Team player with a commitment to continuous learning (both scientific and technical).

How to Apply

Submit your CV and cover letter by July 15 for consideration. In your cover letter, please detail how this training program aligns with your career goals.

Why Novartis

At Novartis, our mission is to reimagine medicine to enhance and extend lives. Driven by our people, we aim to become the most valued and trusted medicines company globally. Join us on this path to achievement! Learn more about our people and culture .

Accessibility and Accommodation

Novartis is committed to providing reasonable accommodations to all individuals. If you require any assistance during the recruitment process due to a medical condition or disability, please email with the job requisition number and your contact information.

Additional Information

Company Name: Novartis

Job Title: Data Science Innovation Fellow - Renal Translational Research