Senior Expert Data Scientist (Oncology) – Data42

Senior Expert Data Scientist (Oncology) – Data42

Summary

Location: Cambridge, US Full time, Onsite

At data42, a division of Biomedical Research, our vision is to empower and inspire Novartis’ research and development community in harnessing inter-connected multimodal internal and external data in a securely governed platform. We strive to accelerate additional research across a continuum of research, Clinical development and medical to unlock insights leading to the discovery and development of new medicines for patients.

Data42 science delivery team is currently seeking a highly skilled individual to join our team as a Senior Data Science Expert. As an essential part of the team, you will collaborate with scientists in various disease and functional areas across the organization to advance research and drug development, specifically in our oncology programs.

With a focus on addressing unmet medical needs in oncology, your responsibilities will involve collaborating with other data42 scientists to design a comprehensive platform for our users and stakeholders to explore while ensuring proper governance.

If you are passionate about making a significant impact in medical research and are eager to join a dynamic and collaborative team, we encourage you to apply. There are numerous diseases with urgent medical needs, and our goal is to address these challenges and create a lasting impact on global healthcare.

About The Role

Major accountabilities:

• Leading projects and effectively communicating with stakeholders and collaborators.
• Actively participating in the design and development of clinical pipeline and customization of pooling trial data as needed to address important scientific questions, such as indication expansion.
• Independently designing, using, and improving bioinformatics tools and models that are specifically designed for integrating different types of data, enabling the exploration of various biological layers.
• Acting as a connector between valuable data resources and project teams, enhancing the generation of hypotheses by sharing insights derived from or applied to late-stage pipeline data.

Essential Requirements

• Preferably PhD in quantitative field such as computer science, data science, mathematics, statistics, or physics. Experience working on problems in the life sciences is a plus.
• At least 2 years of professional experience in the pharmaceutical or clinical sector.
• Experience in at least one of the following: - Using biostatistics methods for inference and hypothesis testing, such as linear and logistic regression or Cox proportional hazard models, and leveraging clinical data for secondary analysis. - Track record of applying AI / deep learning to scientific data. - Experience in using machine learning models to predict clinical endpoints using multimodal data.
• Experience with Python and R scientific stacks, track record of building custom scientific software using best practices (version control, testing, documentation).
• Ability to work as part of an intercultural and interdisciplinary team, including biologists, chemists, and data scientists. Strong communication skills in verbal, written, and virtual formats.

Nice To Have

• Hands-on expertise and a proven track record in utilizing patient/clinical data to generate actionable hypotheses.
• Knowledge of computer programing language SPARK and Experience in using Foundry for data analysis.
• Published research in machine learning conferences such as ICML, ICLR, NeurIPS, or comparable venues.

Why Novartis:

Helping people with disease and their families takes more than innovative science. It takes a community of smart, passionate people like you. Collaborating, supporting and inspiring each other. Combining to achieve breakthroughs that change patients’ lives. Ready to create a brighter future together? Learn more

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EEO Statement

The Novartis Group of Companies are Equal Opportunity Employers. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, sex, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status.

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