Bioinformatics Machine Learning Internship Jobs in Oregon

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.
• Project Management: Oversee and manage RWD and genomics projects from inception to completion. Ensure that projects are completed on time, within budget, and meet high-quality standards.
• Cross-Functional Collaboration: Work closely with other departments such as R&D, Data Science, Business Development, Medical Affairs, Product Management, and Engineering to integrate genomics and clinical data into broader research and development initiatives.
• Reporting and Communication: Present complex RWE data and analyses in a clear and comprehensible manner to a variety of audiences, including non-experts. Prepare detailed reports and publications.
• Innovation and Development: Stay abreast of the latest developments in genomics and bioinformatics. Propose and develop new methods and technologies for advanced data analysis.
• Stakeholder Engagement: Engage with key stakeholders to define project goals, report progress, and discuss findings. Act as a liaison between the technical team and non-technical stakeholders.

Desired qualifications:

• Ph.D. in Bioinformatics, Computational Biology, Genetics, or a related field
• At least 10 years of relevant experience 
• Proven expertise in bioinformatics, particularly in genomics data analysis. Demonstrated expertise in cancer genomics, including genomic alterations, molecular pathways, and cancer biology
• Expert knowledge of bioinformatics tools including mapping, variant calling, CNV analysis and statistical methods
• Strong experience in managing, querying, and analyzing massive-scale genomic and healthcare datasets using SQL, Python, or R, and data visualization tools
• Additional expertise in germline genetics, particularly in relation to organ health and prenatal health, is a significant plus.
• AI/ML Expertise: Proficiency in predictive analytics, advanced machine learning, deep learning, and statistical modeling. Strong hands-on experience with AI frameworks such as PyTorch, TensorFlow, Keras, etc.
• Understanding of real-world clinical data, such as electronic health records, claims data, patient registries, health surveys. Familiarity with common data models (e.g., OMOP) and experience utilizing NLP/LLM to parse unstructured clinical data. 
• Ability to interpret clinical endpoints, understand patient cohorts, and collaborate with clinical stakeholders
• Knowledge of translational medicine and/or early discovery in the biotech or pharmaceutical industry is a plus
• Excellent project management skills with a proven track record in leading successful projects
• Experience managing one or more direct/indirect reports.
• Exceptional communication skills, demonstrating the ability to translate complex models and RWD findings to both technical and clinical/non-technical stakeholders. Ability to produce high quality written documentation for varying audiences
• Proven experience collaborating with cross-functional teams including clinicians, scientists, biostatisticians, regulatory and stakeholders.