Senior Data Scientist - Pharmacovigilance

Placements24; Kimberley; Hybrid; CAD 120,000 - 170,000; Full time

Benefits offered by this job

Bonus opportunities

Health insurance

Retirement plan

Remote work

Home office stipend

Learning & development

Job summary

Placements24 is seeking a highly analytical Senior Data Scientist to join a remote pharmacovigilance team, focusing on drug safety surveillance through ML and statistical modeling. You will analyze large datasets of adverse event reports and real-world data to identify safety signals and inform risk mitigation, contributing remotely to global safety initiatives.

The ideal candidate holds a PhD or Master’s in a quantitative field, with 5+ years of data science experience in pharmacovigilance, and

Qualifications

• Ph.D. or Master's degree in Data Science, Statistics, Computer Science, Bioinformatics, or a related quantitative field.

Responsibilities

• Develop and implement predictive models and algorithms to detect safety signals from diverse data sources.
• Analyze adverse event databases, literature, and real-world data to identify trends and potential safety concerns.
• Build and deploy machine learning models for classification, clustering, and anomaly detection in pharmacovigilance.
• Collaborate with pharmacovigilance physicians, epidemiologists, and regulatory experts to interpret data and guide safety decisions.
• Visualize complex data and communicate analytical findings effectively to both technical and non-technical audiences.
• Ensure data quality, integrity, and compliance with relevant privacy regulations.

Job description

About the Role

Our client is seeking a highly analytical and experienced Senior Data Scientist to join their remote pharmacovigilance team. This role is critical in leveraging advanced data analytics and machine learning techniques to enhance drug safety surveillance and risk management. You will analyze large datasets of adverse event reports, clinical data, and real-world evidence to identify safety signals and inform risk mitigation strategies. The ideal candidate possesses a strong background in statistical modeling, machine learning, and data mining within a pharmaceutical or healthcare context, contributing remotely to global safety initiatives.

Key Responsibilities

• Develop and implement predictive models and algorithms to detect safety signals from diverse data sources.
• Analyze adverse event databases, literature, and real-world data to identify trends and potential safety concerns.
• Build and deploy machine learning models for classification, clustering, and anomaly detection in pharmacovigilance.
• Collaborate with pharmacovigilance physicians, epidemiologists, and regulatory experts to interpret data and guide safety decisions.
• Visualize complex data and communicate analytical findings effectively to both technical and non-technical audiences.
• Ensure data quality, integrity, and compliance with relevant privacy regulations.

Requirements

• Ph.D. or Master's degree in Data Science, Statistics, Computer Science, Bioinformatics, or a related quantitative field.
• Minimum of 5 years of professional experience in data science, with a significant portion focused on pharmacovigilance or drug safety.
• Expertise in statistical modeling, machine learning techniques, and programming languages (e.g., Python, R).
• Experience working with large, complex datasets and databases.
• Familiarity with drug safety databases and adverse event reporting systems.
• Excellent analytical, problem-solving, and communication skills, with the ability to work effectively in a remote environment.

Benefits

• Very competitive annual salary and substantial bonus opportunities.
• Comprehensive health, dental, and vision insurance.
• Generous 401(k) or equivalent retirement plan with employer match.
• Remote work flexibility and a home office stipend.
• Opportunities for continuous learning and professional development in cutting-edge data science applications.
• Significant impact on ensuring patient safety and the responsible use of medicines.