Senior Data Scientist - Safety Analytics & Signal Detection
Career Category
Group Purpose
Job Summary
The Senior Data Scientist applies advanced expertise in statistics, data science, and analytical methodologies to support pharmacovigilance, safety signal detection, post-marketing surveillance, and safety decision-making within Global Patient Safety. The role works closely with Therapeutic Area Safety, Integrated Signal Management, and cross-functional partners to design and execute complex analyses using internal safety data, spontaneous reporting systems, real-world data, and other safety-relevant data sources.
This role applies and evaluates a range of analytical approaches including frequentist and Bayesian signal detection methods, temporal and trending analyses, observational data methods, and emerging machine learning and artificial intelligence techniques. The role is responsible for translating complex safety questions into appropriate analytical designs, developing reproducible and scalable analytical workflows, evaluating methodological assumptions and limitations, and communicating scientifically defensible findings to technical and non-technical stakeholders.
Through the application of advanced analytics, automation, visualization, and innovative data science methods, the Senior Data Scientist helps strengthen Amgen's safety surveillance capabilities, improve analytical efficiency, and enable timely, data-driven decisions that support patient safety.
Key Activities
- Provides data science and statistical support for safety surveillance, signal detection, and signal assessment activities.
- Partners with TA Safety and Integrated Signal Management to develop and execute analytical approaches for safety questions and post-marketing surveillance.
3. Applies frequentist, Bayesian, temporal, and other fit-for-purpose statistical methods used in pharmacovigilance.
- Analyzes internal safety data, spontaneous reporting data, real-world data, and other relevant sources to generate actionable insights.
5. Develops reproducible analytical workflows using programming, automation, and visualization technologies.
- Evaluates and applies advanced analytics, machine learning, NLP, and AI-enabled approaches to support safety surveillance and analytical efficiency.
Knowledge and Skills
Strong background in statistics and complex statistical analysis.
Proficiency in one or more analytical programming languages such as Python, R, SQL, or SAS
Working knowledge of machine learning and emerging AI methods
Strong communication skills.
Ability to lead/influence cross-functional teams.
Ability to manage multiple projects and priorities simultaneously.
Preferred
Knowledge of pharmacovigilance principles, safety signal detection, signal management, and post-marketing surveillance.
- Familiarity with pharmacovigilance and safety data sources, including spontaneous reporting systems, safety databases, and real-world data sources.
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Education (Basic)
- Doctorate degree and 2 years of Data Science / Statistics / Biostatistics / Epidemiology / Mathematics / or related quantitative experience; OR
- Master’s degree and 6 years of Data Science / Statistics / Biostatistics / Epidemiology / Mathematics / or related quantitative experience; OR
- Bachelor’s degree and 8 years of Data Science / Statistics / Biostatistics / Epidemiology / Mathematics / or related quantitative experience; OR
- Associate’s degree and 10 years of Data Science / Statistics / Biostatistics / Epidemiology / Mathematics / or related quantitative experience;
Education & Experience (Preferred)
8+ years of applied data science, statistics, analytics, or related quantitative experience
- Experience in pharmaceutical, biotechnology, pharmacovigilance, clinical safety, epidemiology, or another regulated healthcare environment
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Experience analyzing adverse event, spontaneous reporting, real-world, claims, EHR, or other healthcare data
Experience with pharmacovigilance signal detection methodologies, including frequentist and/or Bayesian approaches
Experience using Python, R, SQL, SAS, or comparable analytical technologies
Experience with machine learning, NLP, or AI-enabled analytical methods is desirable
- Degree in Statistics, Biostatistics, Mathematics, Data Science, Epidemiology, Computer Science, Engineering, or another quantitative/scientific field
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Not included in the source posting: what you'll do, benefits.
Skills
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