Job Description

Job Description

A recognized company in New Jersey is actively seeking a new Senior Scientist to join their growing Health Economics Resources initiatives. In this role, the Senior Scientist will be responsible for supporting the design, execution, and communication of observational studies using diverse RWD sources.

Responsibilities:

• Support implementation of strategies to demonstrate the unmet need in disease with focus on outcomes meaningful to payers, clinical decision-makers, and regulators
• Assist with research to generate evidence necessary to demonstrate product differentiation and value for commercial/market access needs support clinical development, regulatory and safety needs (real-world evidence and epidemiology studies)
• Partner with CORE asset leads in development and execution of RWE studies delivered through hands-on analytics execution
• Evaluate new and emerging data modalities (e.g., claims, EHR, social determinants of health, genomics, biomarkers, clinical notes) and conduct feasibility assessments to match study objectives with optimal RWD sources (claims, EHR, registries, patient-generated data)
• Execute studies by managing table shells, analytic data file, analysis plan, programming, statistical methods, and quality control per regulatory and scientific standards
• Conduct quality assurance and code validation, review programming code and data
• Draft analysis reports and support the final publication/presentation of results
• Perform other duties, as needed

Qualifications:

• 3+ years of experience in Real-World Evidence Generation and Epidemiology Analytics
• Graduate (PhD or Masters) degree in Epidemiology, Biostatistics, Public Health, or related field
• Working knowledge of the structure and caveats of Healthcare Claims Databases, Electronic Medical Records and/or Hospital Billing Data, Cancer Registry Data (SEER), Linked Dataset, etc.
• Knowledge in Epidemiologic and Statistical concepts, such as confounding, bias, cumulative incidence, incidence rate, regression models, survival analyses.
• Knowledge of Research Design and an ability to apply appropriate Statistical Methods common in outcomes research & epidemiology (survival analysis/modeling, regression analysis, IPTW, MAIC, causal inference, etc)
• Hands-on proficiency in Statistical Programming (SAS, R, Python) on Real-World Oncology Claims / EHR Data & AI/ML frameworks
• Solid problem solving and time management skills
• Great interpersonal skills
• Excellent communication skills (written and verbal)
• Strong attention to detail
• Highly organized
• Able to multitask efficiently and effectively

Desired Skills:

• Oncology experience
  

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