Staff Scientist, Bioinformatics/RWD

Natera•US Remote, OR

10d•$158,000 - $197,500

About The Position

Natera is seeking an innovative, driven bioinformatics scientist to lead and conduct real-world evidence (RWE) analyses and predictive analytics across oncology, organ health, and women’s health datasets. The role combines bioinformatics, artificial intelligence (AI), machine learning (ML), and analysis of complex real-world data (RWD). The scientist will use advanced AI methodologies to derive actionable clinical insights from multimodal genomic, clinical, and demographic data, communicate findings through effective data visualization, and advance the application of genomics in real-world clinical settings. Strong project management and communication skills are important.

Requirements

Ph.D. in Bioinformatics, Computational Biology, Genetics, or a related field.
At least 10 years of relevant experience.
Expertise in bioinformatics and genomics data analysis, including cancer genomics, genomic alterations, molecular pathways, and cancer biology.
Expert knowledge of bioinformatics tools and methods, including mapping, variant calling, CNV analysis, and statistical methods.
Strong experience managing, querying, and analyzing large-scale genomic and healthcare datasets using SQL, Python, or R, and data visualization tools.
Proficiency in predictive analytics, advanced machine learning, deep learning, and statistical modeling, with hands-on experience using AI frameworks such as PyTorch, TensorFlow, or Keras.
Understanding of real-world clinical data, including EHR, claims data, patient registries, and health surveys.
Ability to interpret clinical endpoints, understand patient cohorts, and collaborate with clinical stakeholders.
Excellent project management skills and a track record of leading successful projects.
Experience managing one or more direct or indirect reports.
Exceptional communication skills, including translating complex models and RWD findings for technical, clinical, and non-technical stakeholders and producing high-quality written documentation.
Experience collaborating with cross-functional teams, including clinicians, scientists, biostatisticians, regulatory professionals, and other stakeholders.

Nice To Haves

Expertise in germline genetics, particularly relating to organ health and prenatal health.
Familiarity with common data models such as OMOP and experience using NLP or LLMs to parse unstructured clinical data.
Knowledge of translational medicine and/or early discovery in the biotech or pharmaceutical industry.

Responsibilities

Lead analysis of large-scale cancer and germline multi-omics datasets, using traditional bioinformatics tools and AI-driven techniques to interpret genomic data for RWE studies.
Lead extraction, curation, and analysis of large-scale RWD, including electronic health records (EHR), claims data, and patient registries; design and execute RWE studies supporting clinical, commercial, and regulatory objectives.
Integrate omics data with clinical, demographic, and other data; manage large datasets while ensuring data integrity and confidentiality.
Develop, train, and deploy AI and ML models, including deep learning, NLP, and ensemble methods, to forecast trends and patient outcomes and support biomarker discovery.
Apply AI frameworks to identify patterns that inform clinical decision-making and product strategy.
Use NLP and LLMs to extract structured insights from unstructured clinical notes, pathology reports, and other RWD sources.
Oversee RWD and genomics projects from inception through completion, ensuring they are on time, within budget, and meet quality standards.
Collaborate with R&D, Data Science, Business Development, Medical Affairs, Product Management, Engineering, and other cross-functional teams.
Present complex RWE data and analyses clearly to technical and non-technical audiences; prepare detailed reports and publications.
Keep current with developments in genomics and bioinformatics and propose and develop new methods and technologies for advanced data analysis.
Engage stakeholders to define project goals, report progress, and discuss findings; serve as a liaison between technical and non-technical stakeholders.