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Job Details


Merck & Co, Inc

Senior Scientist, Machine Learning and Genomics/Genetics

Agriculture and Forestry

Soil And Plant Scientist

No

Cambridge, Massachusetts, United States

Job Description

Our Research Scientists are our Inventors. We identify and target steps in disease mechanisms or pathways that could be inhibited or enhanced. Our goal is to isolate a compound that is effective against a disease target. Using innovative thinking, state-of-the-art facilities and robust scientific methodology we collaborate to discover the next medical breakthrough.

Our company is a global health care leader with a diversified portfolio of prescription medicines, vaccines and consumer health products, as well as animal health products. Today, we are building a new kind of healthcare company – one that is ready to help create a healthier future for all of us. Our company is on a quest for cures and is committed to being the world’s premier, most research-intensive biopharmaceutical company. To this end, our company is intentionally at the forefront of applying genetics, genomics, biology and data science to advance breakthrough science and drug development across therapeutic areas.

Our ability to excel depends on the integrity, knowledge, imagination, skill, diversity and teamwork of people like you. To this end, we strive to create an environment of mutual respect, encouragement and teamwork. As part of our global team, you’ll have the opportunity to collaborate with talented and dedicated colleagues while developing and expanding your career.

The Data & Genome Sciences (DGS) department is currently seeking a Senior Scientist with extensive expertise in machine learning and its application to genomics/genetics. Our mission in the Early Discovery Genetics group within DGS is to identify novel targets and biomarkers anchored in human genetics and genomics that will enter our company's drug discovery pipeline. The successful candidate will apply machine learning approaches to identify novel drug targets by leveraging the large-scale biobank data (e.g., UK Biobank, FinnGen, Genes and Health, Our Future Health) and omics data (e.g., transcriptomics, proteomics, epigenetics). The candidate will be part of the Statistical Genetics group and will closely collaborate with geneticists, computational biologists, and biologists in target validation groups and therapeutic areas (e.g., cardiometabolism, ophthalmology and neuroscience). The successful candidate will be team-oriented, with the ability to effectively communicate and thrive in an inclusionary, integrated, and multidisciplinary work environment.

Specifically, the Senior Research Scientist will:

  • Build and apply machine learning models to large-scale genetics, EHR, and omics data to discover novel drug targets with high probability of success and to identify novel pathways, biomarkers, and causal mechanisms of drug targets across cardiometabolic, ophthalmology and neuroscience disease areas.

  • Participate in large academic/industry partnerships and leverage data from large biobanks (including FinnGen, UK Biobank, Genes and Health, and Our Future Health).

  • Ensure that our company is at the forefront of machine learning and is applying the most advanced methods and tools in this constantly evolving field.

  • Closely collaborate with scientists in DGS and the therapeutic areas to conceptualize, execute and apply machine learning research for target identification and validation.

  • Communicate results to program teams and therapeutic area leadership.

  • Provide machine learning expertise to biologists and senior leadership across disease areas.

Education:

  • PhD., preferably with 3+ years of academic or industry postdoctoral experience in Computer Science, Statistics, Engineering, Computational Biology, Biophysics, or a related discipline.

Required:

  • Solid Machine Learning background and experience in its application to genetics and genomics with a proven publication record.

  • Proficient in building machine learning models for genetics, omics, and/or biobank data.

  • Proficient in one or more programming languages (e.g., Java, Python, R).

  • Experience in the Linux environment, high-performance cluster, and/or cloud computing (AWS, Google cloud).

  • Strong communication, collaboration, and leadership skills including ability to distill and communicate complex machine learning concepts to scientists from diverse disciplines.