Amgen is hiring for the position of Senior Data Scientist – Safety Analytics & Signal Detection in its Global Patient Safety function in Hyderabad, India. The role focuses on pharmacovigilance analytics, statistical signal detection, post-marketing safety surveillance, and advanced data science applications to support patient safety decisions.
The selected candidate will work with internal safety data, spontaneous reporting systems, real-world data, and other safety-relevant sources. The position requires expertise in statistical analysis, analytical programming, machine learning, and AI-enabled methodologies.
Job Details
| Particulars | Details |
|---|---|
| Company | Amgen |
| Job Role | Senior Data Scientist – Safety Analytics & Signal Detection |
| Job Requisition ID | R-253142 |
| Job Category | Safety |
| Job Type | Full Time |
| Work Mode | On-site |
| Location | Hyderabad, India |
| Experience | 8–13 years preferred |
| Department | Global Patient Safety |
| Application Status | Posted Today |
Position
Senior Data Scientist – Safety Analytics & Signal Detection
The role is part of the Integrated Signal Management group, which oversees safety signal detection and management, safety governance, quality complaint trending, and analytics.
Key Responsibilities
- Provide data science and statistical support for safety surveillance, signal detection, and signal assessment activities.
- Collaborate with Therapeutic Area Safety and Integrated Signal Management teams to develop analytical approaches for safety questions and post-marketing surveillance.
- Apply frequentist, Bayesian, temporal, and other statistical methods used in pharmacovigilance.
- Analyze internal safety data, spontaneous reporting data, real-world data, and other relevant safety information.
- Develop reproducible analytical workflows using programming, automation, and visualization technologies.
- Apply machine learning, natural language processing (NLP), and AI-enabled analytical methods to improve safety surveillance.
- Evaluate analytical methodologies, assumptions, and limitations.
- Communicate analytical findings, methods, limitations, and recommendations to technical and non-technical stakeholders.
Eligibility and Qualifications
Candidates must meet one of the following basic education and experience requirements:
- Doctorate: PhD or equivalent doctoral degree with 2 years of relevant quantitative experience.
- Master’s Degree: Master’s degree with 6 years of relevant quantitative experience.
- Bachelor’s Degree: Bachelor’s degree with 8 years of relevant quantitative experience.
- Associate’s Degree: Associate’s degree with 10 years of relevant quantitative experience.
Relevant fields include:
- Statistics
- Biostatistics
- Mathematics
- Data Science
- Epidemiology
- Computer Science
- Engineering
- Other quantitative or scientific disciplines
Preferred Experience
- 8–13 years of applied data science, statistics, analytics, or related quantitative experience.
- Experience in pharmaceutical, biotechnology, pharmacovigilance, clinical safety, epidemiology, or regulated healthcare environments.
- Experience analyzing adverse event, spontaneous reporting, real-world, claims, or electronic health record (EHR) data.
- Experience with pharmacovigilance signal detection methodologies, including frequentist and Bayesian approaches.
- Hands-on coding experience using Python, SAS, R, and SQL or comparable analytical technologies.
- Experience with machine learning, NLP, or AI-enabled analytical methods.
Required Skills
Candidates should have the following technical and professional skills:
- Statistical Analysis: Strong understanding of statistics and complex statistical methodologies.
- Programming: Proficiency in Python, R, SQL, or SAS.
- Machine Learning and AI: Working knowledge of machine learning and emerging AI methods.
- Pharmacovigilance Analytics: Understanding of safety signal detection, signal management, and post-marketing surveillance.
- Bayesian and Frequentist Statistics: Knowledge of statistical approaches used in safety surveillance.
- Healthcare Data Analytics: Familiarity with spontaneous reporting systems, safety databases, and real-world data sources.
- Communication: Ability to present complex analytical findings clearly.
- Stakeholder Management: Ability to lead and influence cross-functional teams.
- Project Management: Ability to manage multiple projects and priorities in a matrixed environment
