Translational Imaging Data Scientist

Key Skills

Java Programming, Python Programming, SQL, Machine learning techniques, Deep Learning, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

20 petabytes of data! 2 million patient-years of information and 30 data domains from preclinical through Phase 3 trials! All waiting for you to unlock the next breakthrough in medicine.

As a member of the Quantitative Sciences & Innovation (QSI) unit in Biomarker Development, you will join Novartis’ clinical trial team to speed new medicines into the clinic across one of the industry’s largest and most innovative portfolios. By integrating diverse biomarker technologies from First In Human through Phase III clinical trials, you will investigate disease heterogeneity, drug efficacy, and patient safety at a molecular resolution to advance trials in new indications and patient populations. You are a passionate and curious scientist, eager to bring creative solutions to biology’s most daunting challenges on our journey to reimagine medicine. Working alongside dedicated physicians and biomarker scientists, our Data Scientists provide the analytical insights that drive Translational success.

Your main responsibilities will include:

• Conceive, design, and execute exploratory analyses as a Lead Imaging Data Scientist across First In Human, Phase I-III clinical trials, applying advanced analytical methods as required
• Conduct and communicate complex statistical analyses of medical imaging studies to support trial design and registration activities.
• Develop novel computational solutions and provide ‘hands-on’ subject matter expertise for integrating clinical, imaging, and high-dimensional data modalities (genetics, genomics, and/or digital) to inform patient stratification and enrichment strategies
• Work expeditiously as a member of matrixed clinical trials teams with diverse membership and expertise

Position can be based in Cambridge, MA or Basel, Switzerland.

Diversity & Inclusion / EEO

The Novartis Group of Companies are Equal Opportunity Employers and take pride in maintaining a diverse environment. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status. We are committed to building diverse teams, representative of the patients and communities we serve, and we strive to create an inclusive workplace that cultivates bold innovation through collaboration and empowers our people to unleash their full potential.

Minimum Requirements

What you’ll bring to the role:

• PhD or equivalent experience in medical imaging analysis, including traditional (e.g., atlas based) and modern machine learning methods (deep learning).
• Three or more years of experience in analyzing large imaging datasets in drug discovery/development or relevant academic setting.
• Expert knowledge in python libraries and deep learning frameworks (e.g., Tensorflow, Pytorch) and working experience with open-source imaging visualization and processing tools, such as 3D Slicer, Freesurfer, ANTS and FSL.
• Strong grasp of the strength and limitations of different imaging modalities (e.g., CT, MRI, PET-CT, ultrasound, OCT, fundoscopy)
• Strong understanding of biostatistics and machine learning, including longitudinal analysis, including R and Matlab.
• Experience with PACS systems and image annotation tools.

WHY CONSIDER NOVARTIS?

• 766 million. That’s how many lives our products touched in 2021. And while we’re proud of that fact, in this world of digital and technological transformation, we must also ask ourselves this: how can we continue to improve and extend even more people’s lives?
• We believe the answers are found when curious, courageous and collaborative people like you are empowered to ask new questions, make bolder decisions and take smarter risks.
• We are Novartis. Join us and help us reimagine medicine.
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