We are seeking an analytically driven and intellectually curious Data Scientist to join the Advance Solutions Team within the DELOS (Data, Exploration & Linked Outcomes Solutions) Research Group. This role will provide technical expertise in real-world data (RWD) analytics, advanced statistical methods, and data science to support cross-functional research initiatives. The ideal candidate combines strong quantitative skills with healthcare domain knowledge and a passion for translating complex datasets into actionable insights that drive evidence-based decision-making.
Responsibilities:
- Lead and execute agile sprints with stakeholders from all business domains, gathering requirements and delivering actionable data solutions.
- Harmonize and integrate patient-level data (genomics, clinical trial, EHR/claims, real-world evidence, etc.) across business lines.
- Partner closely with project owners to ensure data, tools, and AI solutions are scalable, fit-for-purpose, and impactful.
- Serve as the point person for high-priority, cross-functional initiatives (e.g., obesity, Mace, indication expansion, combo therapy, IL31, TL1A).
- Contribute to the organization’s long-term data/AI/tool strategies by sharing hands-on knowledge and workflow improvements.
- Drive engagement, adoption, and change management by actively collaborating with teams from early research through commercial and M&A.
- Serve as a key contributor on the Advance Solutions Team within DELOS, working in close partnership with cross-functional research teams and becoming a trusted analytical partner to stakeholders across Medical, Value & Evidence, and Health Economics & Outcomes.
- Design and execute real-world data (RWD) research projects utilizing healthcare databases (insurance claims, EHR, and clinical trial data), collaborating with clinicians, epidemiologists, and statisticians to ensure methodological rigor and external validity.
- Develop and implement complex SQL queries and data engineering pipelines to define patient cohorts and structure data for downstream analysis in R, Python, and Qlik Sense dashboards.
- Apply advanced statistical and machine learning methods — including survival analysis, hypothesis testing, disease prevalence modeling, clustering, and predictive modeling — to extract insights from large, complex healthcare datasets.
- Build, maintain, and optimize analytical tools and Qlik Sense dashboards that deliver actionable insights to key stakeholders.
- Proactively identify new data science approaches and methodologies that improve the team’s ability to answer research and business questions, staying abreast of leading-edge techniques.
- Work with stakeholders to define research questions, requirements, timelines, objectives, and success criteria for data science initiatives.
- Ensure data science projects are conducted on time, accurately, within budget, and in accordance with financial and ethical compliance guidelines.