Senior Data Scientist - Safety Analytics & Signal Detection

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

1. Provides data science and statistical support for safety surveillance, signal detection, and signal assessment activities.

2. 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.

4. 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.

6. Evaluates and applies advanced analytics, machine learning, NLP, and AI-enabled approaches to support safety surveillance and analytical efficiency.

7. Communicates analytical methods, findings, limitations, and recommendations clearly to cross-functional stakeholders.

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.

Ability to operate effectively in a cross-functional, matrixed environment.

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.

Knowledge of frequentist and Bayesian statistical approaches used in safety surveillance and signal detection

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)

8-13 years of applied data science, statistics, analytics, or related quantitative experience

Experience in pharmaceutical, biotechnology, pharmacovigilance, clinical safety, epidemiology, or another regulated healthcare environment

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 OR SAS OR R and SQL or comparable analytical technologies hands with coding

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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