Clinical Data Scientist

Firma apreel powstała w kwietniu 2010 roku. W miarę rozwoju firmy i równolegle ze wzrostem poziomu zaufania klientów, jej działalność poszerzyła się o usługi Outsourcingu Specjalistów IT. Dziś to właśnie ten obszar stanowi główny filar działalności apreel.

• 3–5 years of hands-on experience working with clinical trial and/or real-world clinical datasets (EHRs, registries, claims).
• Strong applied experience in data analysis, and reporting using Python, R and SQL; production-ready, reproducible and documented code.
• Experience with Statistical Modeling, Machine Learning and Deep Learning.
• Familiarity with clinical data standards and transformations: CDISC (SDTM, ADaM), OMOP, and HL7.
• Experience with data standards mapping, CDISC implementations, or clinical trial design/operations.
• Experience with GitHub and Databricks or similar enterprise data platforms for scalable analytics and collaboration.
• Demonstrable experience defining cohorts, performing subpopulation/stratified analyses, and establishing QA checks for analytical outputs.
• Comfortable working with imperfect, heterogeneous clinical data and pragmatic about delivering timely, high-quality insights.
• Strong problem-solving skills, critical thinking, and meticulous attention to detail.
• Excellent verbal and written communication skills; able to convey complex analyses to clinical, technical, and product audiences.
• Results-oriented with strong ownership, learning agility, and collaborative mindset.
• 3 lata doświadczenia na podobnym stanowisku
• Znajomości: SQL, Python
• Języki: Polski, Angielski, Rosyjski

• Execute exploratory and confirmatory analyses on clinical trial and real-world datasets to inform clinical strategy and trial design.
• Design and implement complex data analysis plans and translate analyses into clear, actionable recommendations for clinical and product stakeholders.
• Rapidly prototype Minimum Viable Products (MVPs) and analytical workflows using agile practices and DevOps principles to iterate with users.
• Define cohorts, design subpopulation analyses, and perform robust quality assurance on outcomes and derived datasets.
• Ensure clinical concepts are correctly represented and harmonized across data models (CDISC SDTM/ADaM, OMOP, HL7); contribute to mapping and transformation logic.
• Produce reproducible code and well-documented workflows in Python, R and SQL; collaborate with engineering to operationalize analyses on Databricks or similar platforms.