Senior Population Health Data Scientist - Causal Inference

Nationwide-Children; Columbus (OH); On-site; USD 120,000 - 160,000; Full time

Job summary

Nationwide Children's Hospital is seeking a Lead Population Health Data Analyst to design and execute advanced analytics for evaluating population health programs and guiding strategic decisions. This senior role partners with clinical, business, and operational leaders to define analytic questions and measure intervention impact using rigorous methods on claims and EMR data.

The candidate will apply causal inference, propensity score matching, and difference-in-differences techniques, and will

Qualifications

• Master’s degree required in a quantitative or related field.
• Experience with Python/R/MATLAB/SAS for analytics and data manipulation.
• Proficiency in statistical modeling, ML, NLP, and geospatial analytics.
• Strong interpersonal and cross-functional collaboration skills.

Responsibilities

• Organizes and analyzes large data sets to develop insights and predictive models.
• Applies statistical and predictive analysis, NLP, geospatial analysis, or ML techniques to healthcare data.
• Identifies patterns and trends; communicates findings to stakeholders.
• Collaborates with cross-functional teams to implement data-driven solutions.
• Mentors less experienced staff and stays current with data science trends.

Skills

Python

R

MATLAB

SAS

NLP

Geospatial analytics

Statistical modeling

Machine learning

Interpersonal communication

Collaboration

Education

Master’s Degree (Computer Science, Engineering, Mathematics, Statistics, Clinical Informatics, Nurse Informatics)

Job description

Nationwide Children's Hospital is seeking a Lead Population Health Data Analyst to design and execute advanced analytics for evaluating population health programs and guiding strategic decisions. This senior role partners with clinical, business, and operational leaders to define analytic questions and measure intervention impact using rigorous methods on claims and EMR data.

The candidate will apply causal inference, propensity score matching, and difference-in-differences techniques, and will