Machine Learning Scientist – Computational Pathology

Website Novartis_CH Novartis Pharma AG

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.

The Pre-Clinical Safety Project Pathology group in the Novartis Institutes for Biomedical Research (NIBR), Basel, is looking for you, an inspiring leader and authority in machine/deep learning (ML/DL) and imaging data science. We seek a computational scientist to help us pioneer machine learning approaches for the analysis of histopathological whole slide images (WSI). Explore new ways to integrate WSI data with various data sources: in vivo (clinical observations, body and organ weights, clinical pathology and biomarker results and omics) and in vitro toxicology data (off target screening), publications, Real World Evidence (RWE), Electronic Health Records (EHR), clinical trials data, assay data, omics, clinical biomarkers and/or other sources to support decision-making for drug discovery.

What you’ll be doing
• We want you to explore novel machine learning methods, extract reliable insight from Whole-slide images (WSI) and identify correlations with multiple related data sources helping us to better understand the mechanisms of toxicity and clinical translatability.
• Provide us with guidance on analysis methods, data visualization, data integration, computational infrastructure, HPC/Cloud workflows for computational pathology.
• Work with different line functions with a focus on digital pathology, secondary pharmacology and multi-modal data integration.
• Join our safety assessment teams with the responsibility of accelerating the application of computational pathology data annotation, analysis and building decision support algorithms.
• Share knowledge with colleagues and train them on ML and computational approaches including model understanding/interpretability.
• Represent us in external and intra-departmental collaborations.
• Engage with the broader data science community within Translational Medicine, with data science partners in functions across Novartis, as well as external partners.

Minimum requirements

What you will bring to the role:

• PhD in computer science, engineering, statistics, mathematics, or related field ideally with applications in biology.
• Strong knowledge of mathematical foundations of machine learning (including deep learning), signal processing and image processing.
• Solid experience in histo(patho)logy image analysis including applications of convolutional neural networks.
• Practical experience in one or more field-relevant programming languages such as Python, C/C++ and R. Hands-on experience with commonly used deep learning frameworks (e.g. TensorFlow, Pytorch) and model architectures.
• Good technical experience and organizational skills to lead complex machine learning projects/initiatives. Demonstrated ability to design, implement and rollout analysis solutions.
• Experience in data visualization and in designing custom practical interactive visual data exploration interfaces for domain specialists (incl. web-based GUI).
• Extensive experience designing and scaling up/implementing large data workflows (especially image data) using a scientific computing environment on Unix/Linux (HPC, AWS/Azure).
• Shown experience in pulling and consolidating data from multiple systems, geographical locations and in varied formats, database design and management.

WHY CONSIDER NOVARTIS?
750 million. That’s how many lives our products touch. 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.

To apply for this job please visit sjobs.brassring.com.

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