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Senior Expert Data Scientist for Cardiovascular, Renal and Metabolism (CRM) - Cambridge Massachusetts
Company: Novartis Group Companies Location: Cambridge, Massachusetts
Posted On: 05/09/2025
Job Description SummaryDo you love data and tech? Are you excited about AI? Are you a team-player who inspires greatness in others? Do you want to find the answer to life, the universe and everything? Join our Data42 team! At Novartis, we reimagine medicine by using the wealth of our internal data combined with advanced analytics. Our Data42 platform provides us access to Novartis's high-quality, multi-modal preclinical and clinical data as well as external data. It is the perfect environment to develop cutting edge AI/ML models. At Data42, we are a global team of data scientists and data engineers dedicated to uncovering novel insights and to drive drug development pipeline decisions by leveraging the Data42 platform. Data42 is currently seeking a highly skilled individual to join our team as a Senior Data Science Expert. As an essential part of the team, you will collaborate with scientists in various disease and functional areas across the organization to advance research and drug development, specifically in our Cardiovascular, Renal and Metabolic programs. Together with the CRM Scientific Lead (Cardio-Renal-Metabolic), you will be part of our Data42 Data Science function. You will collaborate with Scientists, Clinicians, Data Scientists in the Cardiovascular, Renal and Metabolism disease area and with our colleagues in the AICS (AI and Computer Science) team. Job DescriptionMajor accountabilities: - Leading projects and effectively communicating with stakeholders and collaborators.
- Develop cutting-edge AI models
- Actively participating in the design and development of clinical pipeline and customization of pooling trial data as needed to address important scientific questions, such as indication expansion, asset differentiations, patient subgroup identification and discovery of novel biomarkers
- Independently designing, using, and improving bioinformatics tools and models that are specifically designed for integrating different types of data, enabling the exploration of various biological layers.
- Acting as a connector between valuable data resources and project teams, enhancing the generation of hypotheses by sharing insights derived from or applied to late-stage pipeline data. Requirements:
- Solid scientific research background, demonstrated ability to ask and answer critical questions using AI/ML
- Effective at communicating and presenting scientific ideas and results to a diverse audience, including peers, stakeholders from different fields, and non-experts
- Track record of rapidly upskilling in new subject areas to reach a high level of competence. Demonstrated a strong willingness to learn and step out of the comfort zone
- Education: PhD in a quantitative field such as Computer Science, Physics, Statistics, Data Science, mathematics or related quantitative field with proven track/record in ML/AI Preferred Technical Skills
- Machine Learning & Data Science: Supervised and unsupervised learning (e.g., Gaussian Mixture Models, DBSCAN, Random Forest, SVM), Deep learning (CNNs, RNNs, Transformers), Feature engineering, Dimensionality reduction (PCA, t-SNE,UMAP), Anomaly detection, A/B testing, Statistical methods for inference, prediction and hypothesis testing (e.g. linear and logistic regression, Cox proportional hazard model, methods to correct for bias), Time series analysis, Data visualization
- Programming & Tools: Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch), R, SQL, MATLAB, Spark, Git, Docker, good coding practices (e.g. version control, documentation), Cloud computing (AWS)
- Data Engineering: Data cleaning and preparation, SQL, model deployment and optimization, processing big data (e.g. with Spark)
- Experience with clinical data or other data types used in the life sciences (e.g. omics, pre-clinical, claims) would be a plusAbility to communicate complex analyses and findings to a diverse audience, including effective data visualization.
- Strong scientific curiosity, initiative, and learning agility.
- Ability to work as part of an interdisciplinary team, including clinicians, biologists, chemists, and data scientists.
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