Data Scientist - Real World Data - Data42

Job Description Summary

Do you love data, technology, and science? Are you passionate about transformative AI? Are you seeking meaningful impact at the intersection of healthcare, data, and innovation?

Join the data42 team at Novartis as a Data Scientist, RWD. In this role, you’ll work on advanced data science initiatives leveraging our global data42 platform—a secure environment housing high-quality, multimodal preclinical, clinical, and external real-world data (RWD), including genetic, omics, and imaging data. You’ll drive discovery and development of life-changing medicines by driving the integration of AI/ML approaches across biomedical research and drug development. If you are enthusiastic about unlocking health insights from diverse Real-world data, this is your opportunity.

As a scientific expert, you will:
- Champion advanced analytics and AI/ML applications to mine RWD (including genetic and imaging modalities) for novel clinical and translational insights.

- Work with researchers to answer complex scientific questions—like biomarker discovery, digital phenotype identification, and accelerating scientific insights, including moonshot goals such as indication finding/ exploration and expansion.

- Work on methodology advancement and strategic projects across disease areas, across the Research, Development and Commercial continuum, integrating multimodal data for maximum impact.

 

Job Description

Major accountabilities:

• execute AI/ML projects focused on mining RWD (including, but not limited to genomic, and imaging modalities) for exploratory and hypothesis-driven analysis. 

• Co-create analytic solutions with stakeholders in R&D, Biomedical Research, and external partners. 

• Drive capability development for integrating new RWD with genetic modalities (e.g., GWAS, polygenic risk scores) and imaging analytics (e.g., radiomics, deep learning on medical images) within real-world evidence projects. 

• Work in multidisciplinary teams to generate scientific impact and foster a collaborative entrepreneurial environment. 

• Support feasibility, design, and conduct of scientific evidence generation studies using RWD, including external control arm development and innovative in-silico trials. 

• Advocate and communicate data-driven strategies, influencing internal and external stakeholders to advance patient outcomes and Novartis’ R&D vision. 

• Working knowledge of generative AI, and how it may be leveraged to create applications/ solutions in support of evidence generation. 

• Represent Novartis/data42 in internal and external forums to share innovations and insights. Participation in Hackathons. 

 

Minimum Requirements:
Qualifications 

• PhD, MD, or equivalent advanced degree in a quantitative field (computer science, genetics, statistics, biomedical engineering, epidemiology, etc.). 

• 5+ years in quantitative/data science roles within life sciences, including deep experience in AI/ML applied to RWD (including, but not limited to genomics/omics, and medical imaging). 

• Strong programming skills in Python, R(optional), SQL, Spark and analytics platforms (HPC, Databricks etc.). 

• Deep understanding of evidence generation methodologies, advanced analytics, and pharmaceutical product development lifecycle. 

• Proven expertise in integrating and analysing multi-modal RWD for medical evidence generation, including data fusion. 

Skills Desired 

• Data Science, Advanced Machine Learning, Artificial Intelligence 

• Real world data (Claims, EHR/ EMR, Registries, Surveys etc.), including it’s technical modalities (-omics, GWAS, Imaging Modality (Radiology, Pathology, Digital Imaging) 

• Experience in Statistical Analysis, Evidence Generation in early and late phase drug discovery development. Commercial is a bonus. 

• Data engineering skills required to enable genAI solutions in support of evidence generation (good to have). 

Languages :

• English.

 

Skills Desired

Clinical Data Management, Data Architecture, Data Governance, Data Integration, Data Management, Data Profiling, Data Quality, Data Science, Data Strategy, Master Data, Waterfall Model