Remote job
Principal Data Architect (Data Science)
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About this role
Role overview Design and deliver data science solutions, from data ingestion and feature development through model deployment and ongoing monitoring. The work combines hands-on machine learning and data engineering with collaboration across technical and business teams; healthcare data knowledge is useful, and the position may be filled at different levels based on experience.
Responsibilities - Build data pipelines that prepare information for feature engineering and machine learning. - Explore structured and unstructured data, identify useful signals, and develop features suited to the problem. - Select, train, tune, and optimize statistical or machine learning models. - Deploy production code and maintain the pipelines and monitoring processes that keep data products reliable. - Protect the accuracy and integrity of data and model outputs. - Document methods and findings, and collaborate with data scientists, engineers, product managers, and other stakeholders.
Requirements - For the Lead level, at least six years of experience across data science and machine learning. - Expert-level SQL and Python skills, with hands-on experience working with structured and unstructured data. - Experience with relational and non-relational databases and data processing tools such as Apache Spark. - Familiarity with data visualization and monitoring tools, automated testing, and continuous integration and delivery. - Experience with Docker and deploying on Google Cloud Platform or another cloud platform.
Nice to have - Consulting or professional services experience. - Healthcare data knowledge or familiarity with healthcare protocols such as HL7, FHIR, or DICOM.
Benefits and work setup The role includes health insurance, paid time off and holidays, sick leave, parental and bereavement leave, an employer-matched retirement plan, and employee referral bonuses. The listed annual salary range is $180,000–$240,000; the applicable range depends on the level assigned and factors including experience and location.