Verily · Dallas
Data & AIRemote
Verily Health is a data platform and technology company purpose-built to power AI-enabled precision health solutions that accelerate research and improve care for individuals and communities. Uniquely positioned at the intersection of technology, data science, and healthcare, Verily transforms multimodal health data into insights, models, and actions that make healthcare more personalized, predictive, and precise.
Responsibilities
As a technical leader, oversee the plan, design, and execution of data modeling, normalization, and transformation activities. These efforts will support the development of an advanced healthcare data architecture that aligns with organizational goals and adheres to informatics standards.
Lead the design of healthcare data modeling and mapping of source data to FHIR, OMOP and STDM. This will involve data normalization, enrichment, and deduplication to enhance data quality and completeness. To ensure accurate representation of clinical concepts, source data will need to be standardized to terminologies and ontologies like SNOMED, LOINC, RxNorm, and others.
Oversee the integration strategy for ingestion of multi-sourced healthcare data focused on ingesting multi-sourced healthcare data received through standard interoperability exchange protocols, such as FHIR, HL7, and X12 ED.
Architect and oversee the design of robust data pipelines that can handle large volumes of data efficiently and reliably, enabling timely analysis and intervention in time-sensitive situations.
Champion data quality initiatives, establishing rigorous standards and processes to ensure the reliability and accuracy of data.
Foster collaboration between technical and non-technical teams, translating complex technical concepts into actionable insight.
Drive the exploration and adoption of emerging technologies and methodologies, such as machine learning or artificial intelligence.
Design documents that effectively communicate data architecture, normalization principles, and harmonization techniques to technical and non-technical stakeholders across the organization.
Apply expertise to solve complex, high-impact data-related issues, ensuring timely resolution and minimizing disruptions to critical operations.
Leverage deep technical knowledge of SQL, Python, R, dbt, and Jupyter Notebook; cloud computing platforms and version control systems (e.g., Git), along with other higher-level and domain-specific programming languages to lead data design and model development efforts aimed at solving impactful problems.
Minimum Qualifications
Bachelor's degree in Computer Science, Mathematics or medical related field and Master's degree in Clinical Informatics
Minimum of 8 years experience working as a clinical informaticist, data architect or data integration engineer in Healthcare or Life Sciences industries
Demonstrated proficiency in established healthcare standards, controlled terminologies (including SNOMED, LOINC, and RxNorm), standard data models (such as FHIR, OMOP, etc), and healthcare interoperability standards and frameworks like HL7, NCPDP, and CCD-A.
Strong skills related to SQL, Python, R, dbt, ETL tooling and Jupyter Notebook. In addition experience with cloud computing platforms and using version control systems (e.g., Git) and collaborative development tools.
Preferred Qualifications
PhD in Clinical Informatics, MD or PharmD
Previous experience developing informatics solutions to support patient care or clinical research.
Knowledge of clinical practice and research. An understanding of operating procedures and workflows within a hospital or ambulatory setting.
Demonstrated expertise in standard healthcare models, such as OMOP and HL7 FHIR R4/R5, as well as healthcare interoperability frameworks and standards including HL7 FHIR, HL7 v2, CDA/CCD-A, and NCPDP.