- Entry level
- No Education
- Salary to negotiate
50-80%. The amount of time the average data scientist spends preparing data. You will help drastically reduce this number to unlock efficiencies in how we discover new drugs.
Passionate about making connections between data sets at scale to unearth more needles from many more haystacks? We are looking to fill a position that sits precisely at this point in early computational drug discovery: between large-scale processed raw data on one side and individual molecular insights on the other side. If you are a versatile data scientist who enjoys casting problems into generic computational solutions to catalyze efficiencies in data-driven drug discovery, this is for you.
Your responsibilities include but are not limited to:
• Engage with computational peers across Novartis Institutes for BioMedical Research to identify recurrent problems that can be solved at scale, focusing on all data domains that are of practical use in drug discovery.
• Design, implement, and maintain robust methods, algorithms, and packages (python, R) that help the computational community solve old and new problems with ease.
• Define, refine and promote the computational glue that is between large-scale data processing (such as NGS pipelines) and insights at very detailed level.
• Ideate and implement visualizations, dashboards & webservices for data dissemination to computational peers as well as to non-computational collaborators.
Internal level: This will be Investigator II or Investigator I subject to education and experience.
What you will bring to the role
• PhD, MSc or BSc degree in a relevant field (mathematics, computer science, bioinformatics, physics, biology, chemistry)
• Advanced/Business-Level English oral and written
• Excellent scripting skills and experience in R and python with a demonstrated ability to develop and deploy packages/modules for end users; proficiency in Linux, git, SQL and relevant software packages are essential
• Algorithmic/computational understanding and experience of efficiently dealing with small and large sets of heterogeneous data
• Familiarity with relevant biological data for drug discovery and experience of communicating it with broad audiences
• A desire to relentlessly improve the status quo in terms of efficiently linking data across domains
• Statistics; machine learning; bioinformatics/ cheminformatics domain knowledge.
• Familiarity with literate programming environments (such as jupyter, R Markdown, Databricks) and/or cloud computing.
WHY CONSIDER NOVARTIS?
750 million. That’s how many lives our products touch. And while we’re proud of that fact, in this world of digital and technological transformation, we must also ask ourselves this: how can we continue to improve and extend even more people’s lives?
We believe the answers are found when curious, courageous and collaborative people like you are empowered to ask new questions, make bolder decisions and take smarter risks.
We are Novartis. Join us and help us reimagine medicine.
Business Unit CBT - NIBR
Company/Legal Entity Novartis Pharma AG
Functional Area Research & Development
Job Type Full Time
Employment Type Regular
About the company
Novartis has a clear mission, focused strategy and strong culture, all of which we expect will support the creation of value over the long term for our company, our shareholders and society. We recognize that our business depends on the creativity, dedication and performance of our associates. We encourage associates to focus on achievement through collaboration and innovation.
A global healthcare leader, Novartis has one of the most exciting product pipelines in the industry today. A pipeline of innovative medicines brought to life by diverse, talented, performance driven people. All of which makes us one of the most rewarding employers in our field.
Our company culture is guided by high ethical standards. Our values help guide the choices people make every day, and they define our culture and help us execute the Novartis strategy in line with our mission and vision.