Head of Data Engineering - Clinical

August 21

🇺🇸 United States – Remote

🏄 California – Remote

Although this job is listed in California, this company may be open to hiring remote in other states.

⏰ Full Time

🔴 Lead

🚰 Data Engineer

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Deep 6 AI

WebsiteLinkedInAll Job Openings

healthcare analytics • unstructured data • data science • artificial intelligence • machine learning

11 - 50 employees

Founded 2015

🤖 Artificial Intelligence

⚕️ Healthcare Insurance

💊 Pharmaceuticals

💰 $17M Series A on 2019-11

Description

• Lead the continued development and enhancement of our clinical data ingestion and comprehension pipeline. • Drive the utilization of core principles in development, including observability, scalability, and end-to-end control. • Support the utilization of advanced AI/ML research through the exposure of raw, canonicalized, and comprehended data in analytics platforms. • Establish and maintain key performance metrics to track the effectiveness of data engineering initiatives. • Foster collaboration with internal and external stakeholders to gather feedback and drive continuous improvement. • Enhance Deep 6 AI's reputation as a leader in AI-driven clinical trials acceleration through thought leadership and industry recognition.

Requirements

• Strong player-coach mentality, with an ability to balance the hands-on needs of leading two groups: data platform (data engineering) and search/enrichment (ML engineering). • Proven record of accomplishment, leading successful large-scale clinical data initiatives within a product-focused environment. • Strong background in applied data engineering and data pipelines, with hands-on experience developing and deploying production-ready models. • Understanding of Software Development Life Cycle and data product development • Experience working with healthcare data, especially HL7 and FHIR. • Deep understanding of streaming data ingestion and ETL processes • Conceptual understanding of ML techniques, particularly NLP (e.g., NER, BERT). • Conceptual understanding of AI techniques like large language models (LLMs), self-learning models (SLMs), and other state-of-the-art approaches. • Experience with database technologies, especially Elasticsearch, PostgreSQL, Amazon Aurora, and DynamoDB. • Demonstrated passion for staying up to date with the latest data engineering and pipeline trends, along with a record of accomplishment of driving innovation.