Data Scientist

Required skills

Requirements

4+ years of data science experience
Proficiency with Python to build data extraction and processing pipelines
Experience with SQL and cloud data warehousing (Azure SQL, AWS)
Experience integrating AI/LLM tools (e.g., Claude, ChatGPT) into extraction and document-processing workflows
Experience building applications that move and transform data across systems (e.g., ETL pipelines, document parsing, system-to-system validation)
Experience shipping production applications rather than purely analytical work
Ability to work independently and translate evolving, ambiguous requirements into working technical solutions
Experience in regulated environments (e.g., life sciences, clinical trials), particularly with study protocols and quality-controlled documentation
Experience with data validation and simulation testing to confirm consistency across systems
Experience designing database schemas for research or clinical study data
Strong communication skills in both spoken and written English

Startup experience
Experience with BI tools (e.g., Power BI)
Experience partnering with quality or clinical operations teams to gather technical requirements from non-technical stakeholders
Bachelor's Degree in Computer Engineering, Computer Science, or equivalent

Design and build a pipeline to automatically extract relevant fields from source documents (Word, Excel, protocols), replacing manual transcription
Integrate AI/LLM-based extraction (e.g., Claude, GPT) to parse unstructured content and convert it into standardized electronic records
Build and maintain a centralized database to store curated, structured study data
Develop logic to generate finalized documents (Word, PDF) containing the components required for trial execution
Build cross-system validation logic to confirm extracted data matches downstream records, keyed against a test compendium
Structure and query curated datasets using SQL-based data warehousing (Azure SQL, AWS)
Partner with internal quality and data teams to align on data curation standards
Document technical approach and data schemas to support maintainability and future handoff

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