Clinical Data Scientist

Role overview

Own data management and transformation for clinical research and real-world evidence studies, from incoming electronic health record (EHR) extracts through database build, quality checks, data lock, and analysis. The work combines clinical data operations with hands-on engineering to turn varied source data into consistent, auditable, study-ready datasets and improve automated imports.

Responsibilities

- Configure electronic data capture (EDC) forms and study databases, and manage their validation, cleaning, reconciliation, quality control, and data lock. - Profile raw EHR data, including FHIR, HL7v2, and file-based extracts, then build repeatable Python or SQL transformations for mapping, standardization, deduplication, and derived fields. - Design and operate EHR-to-EDC ingestion approaches, including API integrations, data model mappings, versioning, and schema changes. - Work with site IT, informatics, and product partners to understand source-system constraints and plan feasible data capture and monitoring. - Maintain study plans, procedures, runbooks, mapping specifications, training materials, and audit-ready records. - Analyze cleaned data and support evidence reporting with descriptive, comparative, or time-to-event methods as appropriate.

Requirements

- Five to seven or more years in clinical research or real-world evidence data management, including database build and ownership of cleaning and quality control. - Practical SQL and Python or R skills for transformation, ETL pipelines, validation, and reproducible workflows. - Hands-on experience converting raw EHR extracts into structured, analysis-ready or EDC-ready data. - Familiarity with EHR data structures and coding systems; experience with API-based ingestion and multiple data sources. - Ability to maintain data management plans, procedures, runbooks, and version-controlled, auditable code. - Strong organization, documentation, analytical judgment, and communication across technical and clinical teams.

Nice to have

Experience with EDC platforms, CDISC standards such as SDTM or ADaM, statistical methods for real-world studies, pipeline orchestration, or cloud data warehouses. Familiarity with AI techniques is also noted.

Benefits and work setup

The organization describes a remote-first setup across the U.S. and EU, with a flexible hybrid team in Tel Aviv. Benefits vary by location and may include medical, dental, vision, retirement, vacation, and other benefits.