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Tier-1 employer and metro location increase competition, while niche pharmacovigilance specialization reduces applicant density.
Requires pharma/pharmacovigilance, safety-data, and regulatory experience, limiting cross-industry transferability.
Explicit degree-and-years alternatives plus regulated pharmacovigilance and technical skill requirements enforce strict shortlisting.
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Design and execute complex safety signal detection analyses using diverse safety data sources including spontaneous reporting and real-world data.
Develop reproducible, scalable analytical workflows employing advanced statistics, machine learning, automation, and visualization.
Translate pharmacovigilance questions into appropriate analytical methods and communicate scientifically sound results to diverse stakeholders.
Experience: At least 8 years of applied data science, statistics, or related quantitative experience is preferred; baseline varies from 2-10 years depending on degree.
Education: Minimum Bachelor’s degree with 8 years or higher qualifications in Statistics, Data Science, Biostatistics, Epidemiology, Mathematics, or related quantitative field.
Technical Skills: Proficiency in Python, R, SQL, or SAS; strong background in advanced statistical analysis and signal detection methods (frequentist and Bayesian).
Domain Knowledge: Familiarity with pharmacovigilance, safety signal detection, post-marketing surveillance, and use of healthcare data sources such as spontaneous reporting systems and real-world data.
Experienced in pharmaceutical or biotechnology regulated healthcare environments related to clinical safety or pharmacovigilance.
Demonstrated ability to handle complex, multidimensional safety data and apply both traditional statistical and emerging ML/AI methods effectively.
Comfortable working in cross-functional, matrixed teams translating complex safety issues into practical, data-driven solutions supporting patient safety.