Staff Scientist - Real World Evidence, Data Science

Responsibilities Define and execute long ‑ term RWE strategies across the Abbott Vascular portfolio, in collaboration with clinical, regulatory, medical affairs, and market access stakeholders. Translate clinical and economic evidence gaps into actionable strategies aligned with business priorities. Lead the design and execution of real ‑ world evidence, health economics, and digital health studies related to Abbott medical devices, using complex data sources such as electronic health records, insurance claims, imaging data, and hospital administrative/billing databases. Support data science initiatives to transform complex healthcare data into clinically actionable insights through machine learning, predictive modeling, and advanced phenotyping approaches. Lead analytic strategy and perform hands ‑ on programming to execute RWE studies using SQL, R, SAS, Python, or similar languages. Evaluate and mitigate technical risks in study designs. Manage data preparation, cohort construction, variable derivation, and analysis across diverse data types. Ensure adherence to data governance standards and privacy requirements. Provide technical oversight of programming logic and validation, partnering with data analysts and data engineers to ensure accuracy, reproducibility, and regulatory readiness. Lead and oversee development of RWE study protocols and reports, regulatory deliverables, conference presentations, and peer ‑ reviewed scientific manuscripts. Communicate with regulatory and reimbursement agencies on study design and results, translating complex analytical methods into clear, clinically meaningful scientific narratives. Mentor other scientists on dataset selection, study design, and presentation of results. Maintain strong and up ‑ to ‑ date knowledge of the clinical landscape in the coronary and peripheral vascular space. Monitor emerging evidence, clinical guidelines, competitive technologies, and unmet clinical needs to inform RWE study design, endpoint selection, and evidence generation strategy. Apply clinical context to interpret real ‑ world data results, assess relevance to intended use populations, and support regulatory and clinical decision ‑ making. Collaborate effectively with peers across biostatistics, clinical affairs, regulatory affairs, medical affairs, health economics, and R&D teams. Represent RWE and observational research perspectives in cross ‑ functional project teams and strategy discussions. Cultivate relationships with key opinion leaders in the industry, including academic researchers, data vendors, and clinical experts, as appropriate. Requirements Masters Degree (± 18 years) Preferred Minimum 9 years, Related work experience with a complete understanding of specified functional area. Comprehensive knowledge and application business concepts, procedures and practices. Uses in‑depth knowledge of business unit functions and cross group dependencies/ relationships. Is recognized as an expert in work group. Works on complex problems where analysis of situations or data requires an in‑depth evaluation of various factors. Exercises judgment within broadly defined practices and policies in selecting methods, techniques and evaluation criteria for obtaining results. Has broad knowledge of various technical alternatives and their potential impact on the business. PhD, DrPH, MS, or equivalent advanced degree in epidemiology, biostatistics, clinical research, public health, outcomes research, biomedical engineering, or a related discipline. 4 – 8 years of experience in real ‑ world evidence, observational research, outcomes research, or related analytical research roles in industry, academia, consulting, or government. Hands ‑ on experience working with real ‑ world data sources, such as electronic health records, administrative claims data, hospital billing data, registries, or similar large healthcare datasets. Deep knowledge in observational study design and epidemiologic methods, including cohort studies, definition of exposures and outcomes, confounding considerations, and sensitivity analyses. Experience in development of RWE study protocols, analysis plans, or study reports, with exposure to regulatory or post ‑ market evidence needs considered a plus. Practical experience performing data analysis and programming using one or more of the following: SQL, R, SAS, Python, or similar statistical/analytical languages. Experience collaborating with data analysts to validate analytical datasets, programs, and study outputs. Experience in development of study reports, abstracts, slide decks, and manuscripts, with mentorship as needed. Familiarity with regulatory or methodological guidance relevant to RWE or observational studies, with the ability to apply guidance under supervision. Background or interest in the coronary or peripheral vascular space or a related cardiovascular therapeutic area (e.g., structural heart, heart failure, rhythm management), with willingness and ability to build domain expertise over time. Demonstrated ability to manage multiple projects or workstreams simultaneously, prioritize tasks effectively, and deliver high ‑ quality work under timelines, with strong attention to detail. Excellent written and verbal communication skills, with the ability to explain analytical concepts and results clearly to cross ‑ functional partners and proven ability to publish scientific manuscripts. Experience working in cross ‑ functional team environments, including collaboration with clinical, regulatory, medical affairs, or health economics, is preferred. Core Competencies Demonstrates expertise in Real‑World Evidence (RWE) study design, health economics, and advanced data analysis using SQL, R, SAS, and Python. Proven ability to translate complex clinical data into actionable insights while ensuring compliance with regulatory standards. Highest‑signal resume keywords Real‑World Evidence Study Design Health Economics Analysis SQL Programming Epidemiologic Methods Cross‑Functional Collaboration Hard Skills SQL R SAS Python Data Analysis Epidemiologic Methods Observational Study Design Statistical Analysis Data Preparation Cohort Construction Soft Skills Excellent Communication Skills Project Management Mentoring Attention to Detail Judgment and Decision‑Making Certifications & Qualifications Masters Degree PhD DrPH MS Industry Keywords Real‑World Evidence Observational Research Health Economics Coronary Vascular Space Clinical Research Tools & Technologies Electronic Health Records Insurance Claims Data Hospital Billing Data Data Governance Standards Machine Learning #J-18808-Ljbffr Jobtailor