Remote
Staff Scientist, Bioinformatics/RWD
About this role
Natera is seeking an innovative and driven bioinformatics scientist to lead and conduct cutting-edge real-world evidence (RWE) analyses and predictive analytics across oncology, organ health, and women’s health datasets. This unique role blends expertise in bioinformatics, artificial intelligence (AI), machine learning (ML), and the manipulation of complex real-world data (RWD). Candidate will leverage advanced AI methodologies to extract actionable clinical insights from vast multimodal datasets (genomics, clinical, demographic), driving impactful data visualization and advancing our application of genomics in a real-world clinical setting.
The ideal candidate should have strong project management skills, and a keen eye for visualizing complex data in an impactful way to advance our understanding and application of genomics in a real-world setting. Key Responsibilities: Bioinformatics & Genomic Analysis : Lead the analysis of large-scale cancer and germline multi-omics datasets to extract meaningful insights. Utilize and augment traditional bioinformatics tools with AI-driven techniques to interpret genomic data within the context of RWE studies RWD/RWE Analysis : Lead the extraction, curation, and analysis of large-scale RWD sources, including Electronic Health Records (EHR), claims data, and patient registries.
Design and execute robust RWE studies to support clinical, commercial, and regulatory objectives. Data Integration and Management : Facilitate the integration of omics data with other types of data (clinical, demographic, etc.) to enrich the analyses. Manage large datasets and ensure data integrity and confidentiality. AI & Predictive Analytics : Develop, train, and deploy advanced artificial intelligence and machine learning models (e.g., Deep Learning, NLP, ensemble methods) to forecast trends, patient outcomes, and biomarker discovery using RWD and genomics data.
Apply state-of-the-art AI frameworks to identify hidden patterns that inform clinical decision-making and product strategy. Unstructured Data Integration : Facilitate the integration of highly complex, multimodal datasets. Utilize NLP and LLMs to extract valuable structured insights from unstructured clinical notes, pathology reports, and other disparate RWD sources. Manage large datasets while ensuring strict data integrity and confidentiality.