Associate Principal Scientist/Associate Director, Real World Evidence

Role Summary

The Associate Principal Scientist/Associate Director, Real World Evidence leads outcome research using real-world data. You will analyze complex data sets, develop algorithms, and create innovative solutions to support data-driven decision making.

Responsibilities

• Real World Data Analysis: Analyze and interpret large volumes of structured and unstructured real-world patient-level healthcare data, including administrative claims, EHR/EMR, disease registries, and public-use databases. Develop machine learning algorithms and statistical/survival analysis models to extract meaningful insights and outcome research evidence.
• Collaboration And Communication: Work closely with stakeholders in outcome research, medical affairs, statistical programming, and IT functions to provide data-driven insights and solutions. Provide data science and real-world data expert inputs in internal and external collaborations. Present research findings in internal and external scientific congress meetings.
• Project Management: Independently lead Real-World Evidence outcome research or advanced AI/machine learning research projects with minimum supervision.
• Continuous Learning And Innovation: Stay current with the latest research and technologies in data science and machine learning. Proactively seek opportunities to improve existing processes and methodologies.

Qualifications

• Required: Proficiency in Machine Learning and Statistical Programming using tools such as R, SAS, or Python, with a strong foundation in model development and data analysis.
• Required: Advanced SQL skills for efficient data querying, manipulation, and transaction management across complex datasets.
• Required: Extensive hands-on experience with Real-World Data (RWD) sources including administrative claims, EHR/EMR systems, patient registries, and public-use databases, with a proven track record of generating Real-World Evidence (RWE).
• Required: Expertise in cohort identification using clinical and therapeutic classification codes such as ICD-9-CM, ICD-10-CM, SNOMED, LOINC, NDC, HCPCS, and CPT.
• Required: Experience in developing study protocols for non-interventional and methodological research studies, including observational and retrospective designs.
• Required: Working knowledge of research project operations, including contracting, procurement, and budget management processes.
• Required: Strong interpersonal and communication skills, with a keen attention to detail, clarity, and precision in documentation and collaboration.
• Required: Ability to manage multiple analytical projects simultaneously, often across diverse therapeutic areas, with effective planning and organizational skills.
• Preferred: Strong foundational knowledge of statistical and machine learning concepts, with practical application in real-world healthcare data contexts.
• Preferred: Proven experience leading Real-World Evidence (RWE) studies within biomedical research or healthcare organizations.
• Preferred: Experience implementing outcome research studies in disease areas including heart failure, PAH, COPD, IBD, Ophthalmology.
• Preferred: Hands-on experience applying large language models (LLMs) such as BioBERT, MedBERT, or similar to clinical data for research purposes.
• Preferred: Demonstrated ability to mentor and support junior team members, fostering growth and collaboration within research teams.
• Preferred: Co-authorship of peer-reviewed publications involving data science methodologies, and/or active participation in data-focused competitions such as datathons, hackathons, or Kaggle challenges—ideally centered on real-world healthcare data.

Education

• Master’s degree in a relevant field (e.g., Epidemiology, Biostatistics, Public Health, Data Science) with a minimum of 5 years of post-graduate experience conducting research using real-world healthcare data.
• Doctoral degree (PhD, ScD, DrPH) in a related discipline with at least 2 years of post-graduate experience in real-world healthcare data research.

Additional Requirements

• Travel: 10%