The posting
Job Description Summary
#LI-OnSite Location: Cambridge
This position is located in Cambridge, MA and does not have the ability to be located remotely. Please only apply if this area is accessible to you.
We are seeking a highly motivated computational scientist to join the Cardiovascular and Metabolic Disease (CVM) research organization at Biomedical Research (BR) in Cambridge, MA. In this role, you will apply human genetics, statistical genetics, and genomic data science to support new therapeutic target discovery and patient selection strategies across CVM diseases. The successful candidate will work closely with geneticists, computational biologists, disease-area scientists, translational researchers, and clinical development colleagues to generate actionable insights from human genetic and genomic data. This is an opportunity to contribute to innovative drug discovery programs and help advance genetically informed approaches to precision medicine.
Job Description
Key Responsibilities
- Analyze and interpret whole exome/genome sequencing data to identify genetic variants, genes, and biological pathways associated with cardiovascular and metabolic diseases.
- Develop and apply statistical genetics and bioinformatics methods for variant annotation, association analysis, burden testing, rare-variant analysis, causal inference, and gene prioritization.
- Support the identification, evaluation, and validation of novel therapeutic targets using human genetic evidence and complementary genomic data sources.
- Contribute to patient selection and stratification strategies for research and clinical programs, including the identification of genetically defined patient subgroups.
- Integrate human genetics results with transcriptomic, proteomic, functional genomics, clinical, and other relevant data to develop disease and target insights.
- Collaborate with cross-functional project teams to define analytical strategies, interpret findings, and translate results into clear scientific recommendations.
- Develop reproducible, well-documented analytical workflows and contribute to best practices for genomic data analysis.
- Communicate results through presentations, written reports, scientific manuscripts, and internal project updates.
- Stay current with advances in human genetics, statistical genetics, genomic medicine, and computational methods relevant to drug discovery and development.
Essential Requirements
- PhD with 2-3+ years or MS with 6+ years of industry experience in human genetics, statistical genetics, computational biology, bioinformatics, epidemiology, biostatistics, or a related field.
- Postdoctoral research experience is preferred.
- Demonstrated experience analyzing whole exome/genome sequencing data, including variant quality control, annotation, filtering, association testing, and interpretation.
- Strong understanding of human genetics and statistical genetics principles, including rare-variant analysis and genotype-phenotype associations.
- Familiarity with genome-wide association studies, phenome-wide association studies, polygenic risk scores, Mendelian randomization, or functional genomics.
- Experience integrating genomic data with clinical, molecular, or multi-omic datasets.
- Programming and data analysis expertise in Python or R, along with proficiency in Linux or Unix-based computing environments.
- Knowledge of workflow management systems (Git), collaborative coding, and reproducible research practices.
- Ability to work with large-scale genomic datasets and use appropriate statistical and computational methods to derive biologically meaningful conclusions.
- Strong scientific communication, collaboration, and problem-solving skills.
Preferred Qualifications
- Experience applying human genetic evidence to target discovery, target validation, or drug development.
Novartis Compensation and Benefit Summary:
The salary for this position is expected to range between $126,000 and $234,000 per year.
The final salary offered is determined based on factors like, but not limited to, relevant skills and
experience, and upon joining Novartis will be reviewed periodically. Novartis may change the published
salary range based on company and market factors.
Your compensation will include a performance-based cash incentive and, depending on the level of the
role, eligibility to be considered for annual equity awards.
US-based eligible employees will receive a comprehensive benefits package that includes health, life and
disability benefits, a 401(k) with company contribution and match, and a variety of other benefits. In addition, employees are eligible for a generous time off package including vacation, personal days, holidays and other leaves.
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.
Accessibility and reasonable accommodations
The Novartis Group of Companies are committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process, or to perform the essential functions of a position, please send an e-mail to [email protected] or call +1(877)395-2339 and let us know the nature of your request and your contact information. Please include the job requisition number in your message.
Salary Range
$126,000.00 - $234,000.00
Skills Desired
Artificial Intelligence (AI), Biostatistics, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Generative AI, Graph Algorithms, Learning Agility, Logistic Regression Models, Machine Learning (ML), Machine Learning Algorithms, Neuro-Linguistic Programming (NLP), Pandas Python Library, Python (Programming Language), R Programming, Stakeholder Engagement, Statistical Analysis, Structured Query Language (SQL), Time Series Analysis
