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Merck · 17 hours ago

Assoc Principal Scientist (Assoc. Director) Real World Evidence Capability Analytics

West Point, PA

Full-time

Hybrid

Lead/Staff, Director/Executive

$140K/yr - $220K/yr

5+ years exp

Merck is seeking a highly skilled and motivated Associate Principal Scientist/Associate Director with expertise in conducting outcome research using real world data. The role involves analyzing complex data sets, developing algorithms, and leading research projects to enhance data-driven decision-making processes.

BiotechnologyHealth CareMedicalPharmaceutical

Comp. & Benefits

H1B Sponsored

Responsibilities

Analyze and interpret large volumes of structured and unstructured real-world patient level healthcare data, including but not limited to administrative claims, EHR/EMR, disease registry, and public-use databases

Develop machine learning algorithms and statistical/survival analysis models to extract meaningful insights and outcome research evidence

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

Independently lead Real-World Evidence outcome research or advanced AI/machine learning research projects with minimum supervision

Stay current with the latest research and technologies in data science and machine learning

Proactively seek opportunities to improve existing processes and methodologies

Qualification

Machine LearningStatistical ProgrammingReal-World Data (RWD)SQLCohort IdentificationData AnalysisProject ManagementCommunication SkillsInterpersonal SkillsAttention to Detail

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

Advanced SQL skills for efficient data querying, manipulation, and transaction management across complex datasets

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)

Expertise in cohort identification using clinical and therapeutic classification codes such as ICD-9-CM, ICD-10-CM, SNOMED, LOINC, NDC, HCPCS, and CPT

Experience in developing study protocols for non-interventional and methodological research studies, including observational and retrospective designs

Working knowledge of research project operations, including contracting, procurement, and budget management processes

Strong interpersonal and communication skills, with a keen attention to detail, clarity, and precision in documentation and collaboration

Ability to manage multiple analytical projects simultaneously, often across diverse therapeutic areas, with effective planning and organizational skills

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

Preferred

Strong foundational knowledge of statistical and machine learning concepts, with practical application in real-world healthcare data contexts

Proven experience leading Real-World Evidence (RWE) studies within biomedical research or healthcare organizations

Experience of implementing outcome research studies in following disease and therapeutical areas - heart failure, PAH, COPD, IBD, Ophthalmology

Hands-on experience applying large language models (LLMs) such as BioBERT, MedBERT, or similar, to clinical data for research purposes

Demonstrated ability to mentor and support junior team members, fostering growth and collaboration within research teams

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