Job Description Summary
About the role:
Job Description
Key responsibilities:
Collaborate with cross-functional teams to curate key experimental and omics datasets with an emphasis on quality and correctness to ensure that our complex scientific data are trustworthy
Perform exploratory data analyses on key experimental and omics datasets
Evaluate and implement automation tools and Artificial Intelligence / Machine Learning (AI/ML) approaches to enhance data curation and Exploratory Data Analysis (EDA) workflows that increase the speed and accuracy of data handling
Collaborate with cross-functional teams to develop and adopt best practices for data engineering
Essential Requirements:
This position will be located at the Cambridge, MA site and will not have the ability to be located remotely. This position will not require travel as defined by the business (domestic and/ or international).
Ideally advanced degree (PhD, MS, or BS) in computational biology, bioinformatics, data science, computer science, or related field with relevant experience
A minimum of 5-8 years in either academia or industry working in an equivalent position in computational biology, bioinformatics, data engineering, or related field
At least 4-5 years of experience working with molecular biology or omics data
Demonstrated statistical and analytic rigor while performing exploratory data analyses and drawing scientific conclusions from experimental data (e.g., scRNAseq, RNAseq, ChIPseq, DNAseq, proteomics, compound screens, or CRISPR screens)
Fluent in one or more programming languages with bioinformatics applications (R or Python)
Knowledge of version control, reproducible workflows, Unix / Linux
Curiosity, creativity, strong organizational skills, solution-oriented problem solving
Ability to work independently, prioritize tasks, determine project next steps, manage multiple projects and stakeholders simultaneously
Excellent written and verbal communication skills, including the ability to explain complex concepts to diverse audiences
The salary for this position is expected to range between $119,700 and $222,300 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 us.reasonableaccommodations@novartis.com 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
$119,700.00 - $222,300.00
Skills Desired
Apache Hadoop, Applied Mathematics, Big Data, Curiosity, Data Governance, Data Literacy, Data Management, Data Quality, Data Science, Data Strategy, Data Visualization, Deep Learning, Machine Learning (Ml), Machine Learning Algorithms, Master Data Management, Proteomics, Python (Programming Language), R (Programming Language), Statistical Modeling