PHARMAFull-time
Senior Data Scientist - Safety Analytics & Signal Detection
By Admin · Pharmacovigilance & Drug Safety
HYDERABAD (IN)ONSITEPOSTED: 9/2/2026
PHARMAFull-time
HIRING ORGANIZATIONAMGENBiotechnology / Pharmaceuticals
ROLE DEFINITION & RESPONSIBILITIES
Job Overview
Full-time Senior Data Scientist role within Amgen's Global Patient Safety organization.
Part of the Safety function and supports the Integrated Signal Management group's strategic objectives.
Focuses on pharmacovigilance, safety analytics, signal detection, post-marketing surveillance and safety-related decision-making.
Applies advanced expertise in statistics, data science and analytical methodologies to evaluate complex safety questions.
Works closely with Therapeutic Area Safety, Integrated Signal Management and other cross-functional stakeholders.
Uses internal safety data, spontaneous reporting systems, real-world data and other safety-relevant data sources.
Supports the development of scalable, reproducible and scientifically defensible analytical approaches that strengthen patient safety surveillance and enable timely data-driven decisions.
Integrated Signal Management & Group Purpose
Supports activities within the Integrated Signal Management group.
Contributes to the direction and strategy for safety signal detection and management.
Supports safety governance activities.
Contributes to quality complaint trending and analytics where applicable.
Supports policies, research, innovation and implementation of best practices in safety data mining.
Applies statistical signal detection approaches.
Supports signal management and signal tracking activities.
Supports product complaint trending and analytical activities.
Contributes to risk management and benefit-risk assessment activities.
Supports analytical capabilities relevant to safety communications.
Helps strengthen safety surveillance through innovative analytical methodologies and technologies.
Safety Surveillance & Signal Detection
Provide data science and statistical support for safety surveillance activities.
Support safety signal detection.
Support signal assessment activities.
Partner with Therapeutic Area Safety and Integrated Signal Management teams.
Develop analytical approaches to address safety-related questions.
Execute analyses supporting post-marketing safety surveillance.
Translate complex safety questions into appropriate analytical designs.
Select fit-for-purpose statistical and analytical methods.
Evaluate analytical findings to generate actionable safety insights.
Support scientifically defensible safety decision-making.
Evaluate assumptions and limitations associated with analytical approaches.
Statistical & Analytical Methodologies
Apply advanced statistical methods to safety data.
Use frequentist statistical approaches for pharmacovigilance and signal detection.
Apply Bayesian analytical and statistical approaches where appropriate.
Perform temporal analyses to identify safety patterns and trends.
Conduct trending analyses using safety-relevant data.
Apply observational data methodologies where appropriate.
Evaluate fit-for-purpose analytical methodologies for specific safety questions.
Support complex statistical analyses.
Apply quantitative methods to evaluate safety signals and surveillance data.
Assess methodological assumptions, strengths, limitations and interpretability.
Communicate analytical limitations and uncertainty appropriately.
Safety Data Sources
Analyze internal safety data.
Analyze spontaneous reporting data.
Work with spontaneous reporting systems relevant to pharmacovigilance.
Analyze real-world data.
Evaluate other safety-relevant healthcare data sources.
Support analyses involving adverse event data.
Potentially work with claims data where relevant.
Potentially analyze Electronic Health Record data where relevant.
Integrate information from multiple healthcare and safety data sources to generate actionable insights.
Apply appropriate analytical methods based on the characteristics and limitations of available data.
Pharmacovigilance & Post-Marketing Surveillance
Support pharmacovigilance activities through advanced analytics.
Apply knowledge of safety signal detection methodologies.
Support signal management activities.
Contribute to post-marketing surveillance.
Analyze adverse event and spontaneous reporting information.
Support evaluation of emerging safety information.
Contribute analytical evidence supporting patient safety decisions.
Support benefit-risk assessment activities through quantitative analysis.
Improve analytical capabilities used for ongoing safety surveillance.
Programming, Automation & Reproducible Analytics
Develop reproducible analytical workflows.
Use programming technologies to support complex safety analyses.
Apply automation to improve analytical efficiency.
Develop scalable analytical processes where appropriate.
Use visualization technologies to communicate data and findings.
Ensure analytical workflows are structured to support reproducibility.
Apply appropriate coding practices for data science and statistical analysis.
Support efficient and repeatable execution of safety analytics.
Improve analytical processes through automation and technology.
Advanced Analytics, Machine Learning & Artificial Intelligence
Evaluate advanced analytical approaches for safety surveillance.
Apply machine learning methodologies where appropriate.
Evaluate Natural Language Processing approaches.
Explore AI-enabled analytical methods.
Apply emerging data science techniques to improve safety surveillance and analytical efficiency.
Assess the suitability, assumptions and limitations of machine learning and AI approaches.
Support innovation in safety data mining and analytics.
Use advanced analytics to improve the speed and quality of safety-related insights.
Communication & Scientific Interpretation
Communicate complex analytical methods clearly.
Present analytical findings to technical stakeholders.
Translate complex quantitative findings for non-technical stakeholders.
Clearly communicate methodological assumptions and limitations.
Provide scientifically defensible recommendations based on analytical findings.
Support cross-functional discussions involving safety, data science and statistical evidence.
Contribute to effective safety decision-making through clear analytical communication.
Cross-Functional Leadership & Collaboration
Work closely with Therapeutic Area Safety teams.
Partner with Integrated Signal Management stakeholders.
Collaborate with cross-functional teams operating within a matrixed environment.
Lead or influence cross-functional teams without necessarily having direct people-management responsibility.
Manage multiple projects and analytical priorities simultaneously.
Operate effectively in a complex cross-functional environment.
Balance competing priorities while maintaining analytical quality and scientific rigor.
Technical & Domain Knowledge
Requires a strong background in statistics and complex statistical analysis.
Requires proficiency in one or more analytical programming languages.
Programming technologies may include Python, R, SQL or SAS.
Requires working knowledge of machine learning.
Requires familiarity with emerging AI methodologies.
Requires strong communication skills.
Requires the ability to influence cross-functional teams.
Requires effective project and priority management.
Requires the ability to work successfully within a matrixed organizational structure.
Preferred Pharmacovigilance Expertise
Knowledge of pharmacovigilance principles is preferred.
Knowledge of safety signal detection is preferred.
Knowledge of signal management is preferred.
Knowledge of post-marketing surveillance is preferred.
Familiarity with pharmacovigilance and safety data sources is preferred.
Familiarity with spontaneous reporting systems is preferred.
Familiarity with safety databases is preferred.
Familiarity with real-world data sources is preferred.
Knowledge of frequentist approaches used in safety surveillance is preferred.
Knowledge of Bayesian approaches used in safety signal detection is preferred.
Education Requirements
Doctorate degree with at least 2 years of relevant experience in Data Science, Statistics, Biostatistics, Epidemiology, Mathematics or another related quantitative discipline; OR
Master's degree with at least 6 years of relevant quantitative experience; OR
Bachelor's degree with at least 8 years of relevant quantitative experience; OR
Associate's degree with at least 10 years of relevant quantitative experience.
Relevant educational disciplines include Statistics, Biostatistics, Mathematics, Data Science, Epidemiology, Computer Science, Engineering and other quantitative or scientific fields.
Preferred Experience
Approximately 8-13 years of applied Data Science, Statistics, Analytics or related quantitative experience is preferred.
Experience within pharmaceutical, biotechnology, pharmacovigilance, clinical safety, epidemiology or another regulated healthcare environment is preferred.
Experience analyzing adverse event data is preferred.
Experience analyzing spontaneous reporting data is preferred.
Experience analyzing real-world healthcare data is preferred.
Experience with claims data is preferred.
Experience with Electronic Health Record data is preferred.
Experience using pharmacovigilance signal detection methodologies is preferred.
Experience with frequentist and/or Bayesian signal detection approaches is preferred.
Hands-on coding experience using Python, SAS or R is preferred.
Experience using SQL or comparable analytical technologies is preferred.
Experience with machine learning, NLP or AI-enabled analytical methods is desirable.
Business & Patient Safety Impact
Supports the strengthening of Amgen's safety surveillance capabilities.
Improves analytical efficiency through automation and innovative data science methods.
Enables timely and data-driven safety decisions.
Provides quantitative evidence supporting patient safety.
Supports proactive identification and assessment of potential safety signals.
Contributes to continuous improvement and innovation in global pharmacovigilance analytics.
REQUIRED VERIFIED SKILLS
Advanced Statistics
Complex Statistical Analysis
Data Science
Pharmacovigilance
Safety Analytics
Safety Signal Detection
Signal Assessment
Signal Management
Post-Marketing Surveillance
Frequentist Statistics
Bayesian Statistics
Temporal Analysis
Trending Analysis
Observational Data Methods
Python
R
SQL
SAS
Analytical Programming
Reproducible Analytics
Automation
Data Visualization
Machine Learning
Artificial Intelligence
Safety Data Mining
Adverse Event Data Analysis
Spontaneous Reporting Systems
Safety Databases
Real-World Data
Healthcare Data Analysis
Statistical Methodology Evaluation
Cross-Functional Collaboration
Project Prioritization
Scientific Communication
COMPENSATION & REWARDS
Salary: Competitive / Disclosed upon candidate shortlisting
No salary range or compensation figures are provided in the supplied official job description. Compensation for a Senior Data Scientist with approximately 8-13 years of quantitative experience may vary based on educational background, advanced statistical expertise, programming capability, pharmaceutical or biotechnology experience and specialized pharmacovigilance knowledge. Candidates should clarify fixed compensation, annual or performance-based incentives, health and insurance benefits, retirement benefits, learning and development support and other total rewards components during the recruitment process. Specialized expertise in safety signal detection, Bayesian analytics, real-world data and AI-enabled pharmacovigilance may strengthen overall market value.
ORGANIZATION CONTEXT & CULTURE
Amgen is a leading global biopharmaceutical company committed to unlocking the potential of biology for patients suffering from serious illnesses. It discovers, develops, manufactures, and delivers innovative human therapeutics, focusing on areas with high unmet medical needs such as oncology, cardiovascular disease, bone disease, and inflammatory diseases.
TEAM SIZE: 25,000+ employees
INDUSTRY: Biotechnology / Pharmaceuticals
EXPECTED CAREER ROADMAP
This Senior Data Scientist role offers a strong long-term career pathway within pharmacovigilance analytics, safety data science and global patient safety leadership. The role provides opportunities to develop specialized expertise in signal detection, Bayesian and frequentist safety methodologies, real-world data analytics, machine learning and AI-enabled pharmacovigilance. With experience, candidates can progress toward Principal Data Scientist, Safety Analytics Lead, Signal Detection Methodology Lead, Global Safety Data Science Lead or leadership positions within Pharmacovigilance Analytics and Integrated Signal Management. The combination of advanced statistics, healthcare data science and safety surveillance also creates opportunities to become a subject matter expert in benefit-risk analytics, real-world evidence, AI applications in pharmacovigilance and global safety strategy.
APPLICATION ADVISORY
Highlight your quantitative experience clearly and align your total years of experience with the education pathway listed in the role. Demonstrate hands-on expertise in Python, R, SAS and SQL, with specific examples of reproducible analytical workflows and automation. Include detailed projects involving statistical modeling, Bayesian or frequentist methods, temporal analysis and healthcare data. Candidates with pharmacovigilance experience should prominently describe adverse event analysis, spontaneous reporting systems, signal detection or post-marketing surveillance projects. If you have worked with real-world data, claims data or EHR data, provide examples of the data source, analytical methodology and business or safety outcome. Prepare for interviews by reviewing pharmacovigilance signal detection concepts, disproportionality analysis, Bayesian and frequentist methods, temporal trend analysis, observational data limitations and benefit-risk assessment. Be ready to explain machine learning or NLP projects, including model assumptions, validation, interpretability and limitations. Demonstrate your ability to communicate complex analytical findings to both technical and non-technical stakeholders.
RECOMMENDED TRAINING
1/5
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