Data Scientist IIRemote

Care Dx is a leading precision medicine diagnostics company advancing care in transplant, specialty oncology, and cell therapy. Through our innovative portfolio of molecular diagnostics, digital health solutions, AI-powered data and analytics, and patient support services, we partner with healthcare providers, patients, and biopharma organizations to help inform clinical decision-making and improve patient outcomes.

At Care Dx, every employee has the opportunity to contribute to innovations that help transform patient care and improve lives.

The Data Scientist II will develop clinically grounded, interpretable AI and analytics solutions that support commercial, medical affairs, clinical research, and enterprise decision-making. The role will translate integrated structured and unstructured data into reliable insights, actionable recommendations, and reusable analytical products.

This role combines applied data science, statistical modeling, machine learning, generative AI, and research collaboration. The Data Scientist II will own defined work streams from problem framing and research design through modeling, validation, documentation, deployment support, and stakeholder communication. Success requires strong quantitative judgment, reproducible development practices, and the ability to communicate methods, limitations, and recommendations clearly to technical, scientific, clinical, and business stakeholders.

• Own analytics and AI work streams for next-best-action and field education capabilities, translating integrated data into actionable recommendations.
• Advance literature intelligence workflows, including search, retrieval, synthesis, evaluation, and validation of AI-generated outputs.
• Develop interpretable machine learning, statistical, and generative AI solutions using structured and unstructured clinical, laboratory, commercial, and enterprise data.
• Build reproducible workflows and reusable components that can progress from research prototypes to reliable applications and decision-support tools.
• Lead analytics research proposals and collaborations, including cohort definition, analysis planning, modeling, interpretation, and communication of results.
• Partner with clinicians, scientists, medical affairs, research teams, and business stakeholders to translate questions into rigorous analytical plans and defensible results.
• Support abstracts, presentations, and research reports while applying appropriate privacy, security, validation, and documentation practices.
• Contribute to and provide backup support for forecasting, Promotion Effectiveness, and Digital Product Impact initiatives.
• Perform exploratory analysis, feature engineering, model development and evaluation, sensitivity testing, and scenario analysis using Python or R and SQL.
• Collaborate with data and application engineers on pipelines, version control, testing, deployment readiness, monitoring, and maintenance; clearly document data lineage, assumptions, limitations, and recommendations.

• Bachelor of Science (BS) or Master of Science (MS) degree in Data Science, Computer Science, Statistics, Biostatistics, Bioinformatics, or a related quantitative field.
• 5+ years of experience in data science, machine learning, advanced analytics, AI engineering, or a related role, including 3+ years in healthcare, diagnostics, biotechnology, clinical research, life sciences, or another regulated environment.
• Strong hands‑on proficiency in Python or R, SQL, and Git-based development workflows.
• Experience developing and validating predictive, descriptive, statistical, or machine learning models using real-world clinical, EHR, laboratory, research, or other complex longitudinal data.
• Ability to produce audit‑ready documentation covering data lineage, methods, assumptions, testing, validation, and model limitations.
• Strong written and verbal communication skills and the ability to collaborate across technical, scientific, clinical, and business teams.

• Experience with AI‑enabled search, natural language processing, literature intelligence, retrieval‑augmented generation, or document synthesis.
• Experience moving analytical prototypes into user‑facing or production workflows, including testing, monitoring, and ongoing support.
• Familiarity with Databricks, cloud data platforms, model lifecycle management, and modern analytics application frameworks.
• Experience contributing to clinical research deliverables such as protocols, abstracts,…