Senior Expert I, Data Science

Job Description Summary

Position Location: onsite, San Diego, CA
#LI-hybrid

*This role is based in San Diego, CA. Please only apply if this location is accessible for you.


The Oncology Data Science group within Biomedical Research supports the Oncology Disease Area with computational biology, Artificial Intelligence / Machine Learning (AI/ML), and data engineering for novel therapeutics across multiple drug modalities. As integrated scientists and engineers, we apply advanced analytics to pre-clinical and clinical projects, enabling progress in target discovery, drug development, and translational and clinical science.
Help us bring innovative drugs to the clinic by analyzing and interpreting multi-dimensional molecular data (‘omics) into target identification, drug development, and patient biomarker discovery.
The Low Molecular Weight (LMW) team at Novartis Biomedical Research Oncology Data Science is seeking a highly motivated Senior Computational Scientist to join our team. With a focus on induced proximity therapeutics, you will collaborate with cross-functional teams in Biomedical Research and Oncology stakeholders to advance efforts in target identification and drug development to support our ground-breaking drug discovery programs.

 

Job Description

Major accountabilities:

• Collaborate closely with interdisciplinary wet-lab and computational scientists to design, analyze, and interpret high-dimensional biological data (e.g., bulk RNA-seq, DNA-seq, CRISPR, drug screening) to inform critical project decision.

• Lead profiling strategies and analysis of high-throughput genomic and phenotypic screening data to inform patient stratification and mechanism of resistance, in support of drug discovery and development.

• Integrate multi-modal internal and external preclinical datasets (e.g., genomics, transcriptomics, pharmacology, and functional screens) to produce translationally relevant insights.
  Apply advanced bioinformatics and machine learning approaches across multi-modal datasets to uncover novel, actionable biological insights and therapeutic hypotheses.

• Develop and implement innovative analytical methods to support emerging technologies and to effectively integrate, interrogate, and visualize multi-dimensional datasets.

• Drive oncology research by leveraging data mining and genomic profiling to identify novel targets for induced proximity modality, elucidate mechanism of action and support patient stratification strategies.

• Communicate integrative analyses and key findings clearly and effectively to diverse audiences, including cross-functional scientific teams and stakeholders.

Qualifications:

• PhD in Computational Biology, System Biology, Bioinformatics, Data/Computer Science, or related field with relevant industry experience.

• Strong knowledge of cancer biology and multi-modal data types such as genomics, transcriptomics, proteomics and phenotypic screening data.
  Proficiency in one or more programming languages for bioinformatics applications (e.g., Python, R) with experience in UNIX/Linux environment, version control, and reproducible workflows.
  Demonstrated statistical rigor and analytic depth in the analysis of high-dimensional omics datasets (e.g., bulk and single-cell transcriptomics, genomics).

• Demonstrated experience leveraging AI‑assisted coding tools (e.g., copilots, code generators, and LLM-based workflows) to accelerate data analysis, model development, and reproducible scientific pipelines.

• Familiarity with data workflows, including preclinical biomarker discovery and validation; survival analysis is a plus.

• Proven ability to work independently, prioritize tasks effectively, define next steps and manage multiple projects and stakeholders in a fast-paced environment.

• Excellent communication skills, with the ability to deliver complex scientific concepts to diverse audiences.
  Curiosity, creativity and a solution-oriented mindset when addressing scientific problems.
  Fluency in English (written and verbal).

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.

To learn more about the culture, rewards and benefits we offer our people click here.

 

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, Graph Algorithms, Learning Agility, Logistic Regression Model, Machine Learning (ML), Machine Learning Algorithms, Nlp (Neuro-Linguistic Programming) And Genai, Pandas (Python), Python (Programming Language), R (Programming Language), Sql (Structured Query Language), Stakeholder Engagement, Statistical Analysis, Time Series Analysis