Data Scientist (Hybrid)

AbbVie•North Chicago, IL

1d•Hybrid

About The Position

AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com . Follow @abbvie on LinkedIn, Facebook , Instagram , X and YouTube. 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.

Requirements

Bachelor’s Degree with 4 years experience OR Masters Degree with 2 years of experience
Strong SQL skills and ability to perform data engineering tasks to structure data for fit-for-purpose analytical needs.
Proficiency in Python and R for statistical analysis, data manipulation, and machine learning applications.
Qlik Sense experience or strong experience in creation of analytical reports and data visualizations.
Knowledge of statistical methods including survival analysis, hypothesis testing, regression, and machine learning models.
Demonstrated ability to learn, understand, and master new technologies.
Strong written and oral English communication skills.
Ability to multitask and work within timelines in a fast-paced environment.
Analytical reasoning abilities, intellectual curiosity, and creativity in problem solving.

Nice To Haves

Background in life sciences or work experience in the pharmaceutical/healthcare industry preferred.
Experience working with real-world healthcare data (claims, EHR, clinical trial data) preferred.

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.