Clinical Data Scientist

apreel was founded in April 2010 by friends who dreamed of creating their own software house. As the company grew and gained client trust, it expanded its services to include IT expert leasing, which is now a main pillar of apreel's business. The founders continue to pursue their passion for creating proprietary IT solutions and aim for continuous improvement.

• 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.
• Required knowledge of Python, SQL, ML, Deep learning, Clinical trial data, Data standards mapping, Cisco, OOP, HL7, Databricks, GitHub.
• Language skills: Polish, English, Russian.

• 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.