Research Scientist II

We are seeking a highly motivated Senior Research Scientist to play a key role in supporting our Therapeutic Areas (TA) in their efforts to better characterize patients/diseases in a real-world context. Focused initially on heart failure (HF), you will be responsible for (1) using patient-derived molecular and phenotypic data to identify treatment-relevant disease subgroups, (2) integrating diverse features from our clinical trials and externally sourced patient cohorts to define novel endotypes and provide real-world context to our novel drug targets, and (3) utilize modern machine-learning methods to derive and communicate insights to non-expert stakeholders in a clear and meaningful way.

In this role you will:

• Develop and apply advanced but explainable AI/ML methods and statistical models to cardiovascular disease
• Collaborate with cross-functional teams across EVP areas to identify and validate new endotypes, biomarkers, and drug targets
• Analyze large patient-derived molecular and phenotypic datasets, to identify patterns and trends that can inform future target discovery and development
• Present findings with clarity and objectivity in internal presentations and publications in scientific journals
• Mentor junior scientists and provide guidance on computational methods and analyses as well as identify and drive new internal and external collaborative opportunities.

Qualifications:

• Master's degree in Bioinformatics, Computational Biology, Computer Science / Machine Learning, Genomics, or related field (PhD candidates will be considered w/min of 1 yr exp)
• 3 years working/professional experience

Our ideal candidate meets the following criteria:

• Has proven track record of delivering cohort analyses and clear interpretation of insights derived from machine learning
• Has strong knowledge of machine learning algorithms and their application to biomedical data
• Has experience with programming languages such as Python or R, version control, and high-performance computing
• Has excellent communication and collaboration skills
• Has the ability to work independently and drive projects forward

You will have access to a diverse set of patient-derived data and have the freedom to integrate these to identify novel disease subgroups, biomarkers, and drug targets. You will play a key role in driving our TA strategies by contributing to data-driven decision making.

We are looking for someone who thinks this sounds exciting and is motivated by keeping pace with developments in machine learning, real-world data, and cardiometabolic disease. Success is demonstrated on several levels including contributing to TA strategy, proposing new disease endotypes via application of explainable AI/ML methods, mentoring junior scientists, and driving external and academic collaborations.