Economic Modeler

Medasource; United States; On-site; USD 120,000 - 180,000; Full time

Job summary

Abbott seeks a highly quantitative Economic Modeler to advance HEOR efforts in evaluating cancer screening, diagnostics, and treatment value. You will design, simulate, and validate models, integrating diverse data sources and communicating findings to technical and non-technical stakeholders.

The role emphasizes Python-based modeling, data integration, and rigorous cost-effectiveness analyses within a global health organization. Collaboration across functions is essential.

Qualifications

• Master's degree or PhD in engineering, health economics, economics, epidemiology, statistics, operations research, applied mathematics, data science, or another highly quantitative discipline.
• Hands-on experience developing sophisticated simulation, microsimulation, screening, or disease-progression models.
• Strong programming skills, particularly in Python.
• Experience working with large or complex clinical, epidemiologic, economic, or healthcare datasets.
• Knowledge of health economics and cost-effectiveness analysis.
• Strong quantitative problem-solving skills with the ability to independently build, test, and troubleshoot models.
• Ability to communicate complex technical concepts to both technical and non-technical stakeholders.

Responsibilities

• Design, develop, and maintain complex economic, microsimulation, screening, and disease-progression models.
• Build and enhance models using Python and other programming languages and analytical tools.
• Integrate clinical, epidemiologic, economic, and real-world data into quantitative modeling frameworks.
• Conduct cost-effectiveness, scenario, sensitivity, and outcomes analyses.
• Develop modeling approaches to evaluate screening strategies, disease pathways, diagnostic technologies, and healthcare interventions.
• Test assumptions, validate model performance, troubleshoot code, and ensure models are technically sound and reproducible.
• Review scientific literature and available evidence to identify appropriate model inputs, assumptions, and methodologies.
• Collaborate with colleagues across HEOR, epidemiology, clinical, data science, and other scientific functions.
• Interpret complex modeling outputs and help translate findings into actionable clinical and economic insights.
• Clearly document modeling methodologies, assumptions, analyses, and results.

Skills

Python

Quantitative analysis

Stakeholder communication

Programming

Education

Master's degree

PhD

Tools

AWS

C++

Job description

Client Overview

Abbott is a global healthcare company with a broad portfolio spanning diagnostics, medical devices, nutrition, and pharmaceuticals. Following its acquisition of Exact Sciences, Abbott has significantly expanded its presence in cancer diagnostics, bringing together Exact Sciences’ expertise in cancer screening and precision oncology with Abbott’s global diagnostics infrastructure and scale. The Cancer Diagnostics business supports patients across the cancer care continuum—from early detection and screening through treatment guidance and recurrence monitoring with technologies including Cologuard®, Cancerguard®, Oncotype DX®, and Oncodetect®.

Job Description

We are seeking a highly quantitative Economic Modeler to support a growing Health Economics and Outcomes Research (HEOR) team within a global healthcare organization. This individual will contribute to the development of sophisticated economic and disease models used to evaluate cancer screening, diagnostics, disease progression, and the clinical and economic value of healthcare interventions.

This role is ideal for someone who enjoys solving complex healthcare problems through modeling, simulation, and programming. The successful candidate will bring strong technical and coding capabilities along with an understanding of health economics and cost-effectiveness research.

What You'll Do:

• Design, develop, and maintain complex economic, microsimulation, screening, and disease-progression models.


• Build and enhance models using Python and other programming languages and analytical tools.


• Integrate clinical, epidemiologic, economic, and real-world data into quantitative modeling frameworks.


• Conduct cost-effectiveness, scenario, sensitivity, and outcomes analyses.


• Develop modeling approaches to evaluate screening strategies, disease pathways, diagnostic technologies, and healthcare interventions.


• Test assumptions, validate model performance, troubleshoot code, and ensure models are technically sound and reproducible.


• Review scientific literature and available evidence to identify appropriate model inputs, assumptions, and methodologies.


• Collaborate with colleagues across HEOR, epidemiology, clinical, data science, and other scientific functions.


• Interpret complex modeling outputs and help translate findings into actionable clinical and economic insights.


• Clearly document modeling methodologies, assumptions, analyses, and results.

Required Qualifications:

• Master's degree or PhD in engineering, health economics, economics, epidemiology, statistics, operations research, applied mathematics, data science, or another highly quantitative discipline.


• Hands-on experience developing sophisticated simulation, microsimulation, screening, or disease-progression models.


• Strong programming skills, particularly in Python.


• Experience working with large or complex clinical, epidemiologic, economic, or healthcare datasets.


• Knowledge of health economics and cost-effectiveness analysis.


• Strong quantitative problem-solving skills with the ability to independently build, test, and troubleshoot models.


• Ability to communicate complex technical concepts to both technical and non-technical stakeholders.

Preferred Qualifications:

• PhD and/or postdoctoral research experience in a quantitative field.


• Experience developing individual-level microsimulation or large-scale population models.


• Modeling experience in oncology, cancer screening, infectious disease, diagnostics, or population health.


• Experience with colorectal, lung, breast, or other cancer screening models.


• Experience with AWS, C++, cloud computing, or high-performance modeling environments.


• Experience working within academia, healthcare, life sciences, biotechnology, diagnostics, or an HEOR/evidence-generation organization.