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Aidoc is a market-leading healthcare AI company that has developed the world's largest clinical frontier model, supporting nearly 50 million patients annually across 2,000+ medical centers globally. The company has raised over $500 million since 2016 and holds a record number of FDA-cleared solutions.
The Clinical Value team evaluates product outputs and performance to demonstrate real-world clinical and operational value to customers. We are seeking an experienced Analytics Engineer to join this team and build the data foundations, infrastructure, and analytical tools that enable this work.
This hybrid role combines analytics engineering with hands-on data analysis. You will translate complex analytical and clinical needs into scalable, reliable, and reusable data solutions. Key responsibilities include:
- Design, build, and maintain data infrastructure supporting Clinical Value analyses, including reusable data models, pipelines, analytical layers, and tools
- Own technical and analytical implementation of complex Clinical Value applications, adapting methodologies to different products, customers, and real-world data environments
- Develop complex analytical solutions into reusable and scalable frameworks for broader team execution
- Identify data-quality issues, technical bottlenecks, and repetitive processes, developing solutions and automation to improve reliability and efficiency
- Collaborate with analysts, Data Engineering, and technical teams to translate analytical needs into effective, maintainable data solutions
- Lead selected hands-on data-analysis projects analyzing complex clinical and operational datasets
- Work with Go-to-Market teams including Customer Success to understand needs and support customer-facing value demonstrations
Required qualifications: B.Sc. or higher in a Scientific or Engineering discipline with 3+ years in Analytics Engineering, Data Engineering, or technical analytics. Advanced Python and SQL skills; hands-on experience designing production data models, pipelines, and analytical layers. Experience with orchestration/transformation tools (DBT, Airflow, or similar) and cloud data platforms (Databricks, Snowflake, BigQuery, AWS). Strong understanding of data modeling, data quality, testing, and maintainability best practices. Hands-on experience integrating AI development tools and agents (Cursor, Claude agents, GitHub Copilot). Strong analytical mindset and ability to work independently with excellent communication skills.
Nice-to-have: Prior experience or passion for healthcare, AI, or medical field; proven experience in project management or technical team leadership.
