Senior Real-World Data Statistician

BioSpace; Indianapolis (IN); On-site; USD 152,000 - 244,000; Full time

Benefits offered by this job

401(k) plan

Pension

Medical, dental, vision benefits

Flexible benefits

Life insurance

Vacation benefits

Well-being benefits

Job summary

Lilly is seeking a Real-World Data Project Statistician in Indianapolis to design non-interventional studies and develop statistical analysis plans for RWE projects. You will work with epidemiologists, data scientists, and medical affairs to ensure robust analyses of real-world data from claims, EHRs, and registries.

The role emphasizes selecting appropriate methods (propensity scores, weighting, causal ML) and ensuring data quality, regulatory alignment, and transparent reporting.

Qualifications

• Ph.D. in Statistics, Biostatistics, Epidemiology, or a related quantitative field.
• Proficient in statistical programming languages/software such as SAS, R, Python, SQL, Spotfire, Stan.
• Experience with large-scale data platforms and common data models (e.g., OMOP/OHDSI, Sentinel, Databricks, Spark).
• Publication record in pharmacoepidemiology, outcomes research, or RWE methodology.
• Interpersonal communication and teamwork skills; leadership and problem solving.

Responsibilities

• Provide input on study protocol and design non-interventional studies per regulatory guidance.
• Select statistical methods for observational data and draft SAPs.
• Collaborate on data quality plans and data fitness-for-purpose assessments.
• Lead projects and influence research decisions across functions.

Skills

Interpersonal communication

Teamwork

Leadership

Problem solving

Education

Ph.D. in Statistics/Biostatistics/Epidemiology

Tools

SAS

R

Python

SQL

Job description

Lilly is seeking a Real-World Data Project Statistician in Indianapolis to design non-interventional studies and develop statistical analysis plans for RWE projects. You will work with epidemiologists, data scientists, and medical affairs to ensure robust analyses of real-world data from claims, EHRs, and registries.

The role emphasizes selecting appropriate methods (propensity scores, weighting, causal ML) and ensuring data quality, regulatory alignment, and transparent reporting.