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ManagerialM.I.HPVDS
Head of PV Data Science
Managerial · Industry · Data Science
Defines and develops overall analytics solutions and data-services vision for PV.
Also known as: PV Data Science Leader
Draft
This competency standard is a working draft prepared for reviewand is not yet an official GPPC standard. Content is derived from the role’s job description and established pharmacovigilance practice, and is subject to change.
Job description
- Define and develop overall analytics solutions and data services vision. Identify opportunities for unlocking value from data assets to improve performance through evidence-based decision making. Solicit and anticipate future needs, ensure PV and Medical Safety database outputs evolve to meet current and future business and regulatory needs.
- Promote a data-driven culture by providing data-driven insightful solutions to complex business and performance problems and develop inspiring actions that shape and inform key business decisions. Manage vendors providing data services to ensure high quality, optimize the value and cost of their activities.
- Collaborate with partners in IT to improve the availability of data and data quality as required for high quality, innovative, analytics solutions.
- Bring in efficiency with innovative solutions; effective usage of new technologies and concepts; developing new analysis opportunities by integrating existing and new data sources.
- Direct an expert team to implement an advanced system for the management of data retrievals and self-service tabulations, listings, and statistical analysis.
- Lead automation of aggregate analysis and reports including PSUR, DSUR, PQR, APR and audit/inspection related outputs. Manage timely delivery of high-quality PV safety listing, analysis and data and ensure compliance with health authority regulations.
- Lead the design and oversee the development of predictive and data-driven solutions and services to ensure drug, device, trial, and patient-level benefit/risk information is available proactively for safety analysis, signal detection and risk management.
- A key contributor to the business team managing PV inspections and addressing all questions related to Data Science.
- Manage global team; Build a deep talent bench by driving top-level talent acquisition, succession planning and development of associates across who are working to their full potential and to build a strong talent pipeline.
- Lead a team of data scientists to advance the science of PV and support safety officers with data extractions and insights.
- Participate in inspection/audit related readiness activities and provide support for internal and external PV audits.
- Engage in continuous improvement of relevant processes.
- Provide adequate support, motivation, encouragement, and effective management for all relevant staff.
- Develop and implement relevant training.
Overall required level: Expert.Draft competency standard for the Head of PV Data Science role. As a managerial role, expert-level knowledge of the managed function and strong oversight, compliance and people-management competencies are expected. Levels are an initial proposal derived from the role's job description and typical pharmacovigilance practice, pending expert review.
Prerequisites
Mandatory prerequisites
- →University degree in medicine, pharmacy, life sciences or a related discipline
- →Relevant pharmacovigilance experience appropriate to the role
Recommendations
- →Working proficiency in English
- →Familiarity with the applicable GVP modules
Concept of levels
Knowledge for each topic is assessed on a four-level scale.
| Level | Expected knowledge |
|---|---|
| N/A | No knowledge required. |
| Basic | Elementary knowledge of the specific regulation; fundamental understanding of the terms and definitions used, a basic understanding of the concept of the regulation, a general overview of what it covers and where it can be found; passive/delegated usage of the regulation. |
| Intermediate | Very good knowledge of the specific regulation; comprehensive understanding of the terms and definitions and of the concept and context of who/when/where/why/how it applies; active usage of the regulation when required. |
| Expert | Excellent knowledge of the specific regulation; in-depth understanding of the concept and of how and why its sections should be implemented and followed, including outcomes and responsibilities for all relevant stakeholders; active usage of the regulation on a daily basis. |
Knowledge
General Knowledge
| Topic | Level |
|---|---|
| Pharmacovigilance principles and Good Pharmacovigilance Practices (GVP) overviewRef: EMA GVP Module I — Pharmacovigilance systems and their quality systems; EU Reg (EC) 726/2004 & Dir 2001/83/EC | Expert |
| The pharmacovigilance system and Pharmacovigilance System Master File (PSMF)Ref: EMA GVP Module II — Pharmacovigilance system master file (Rev 2) | Expert |
| Individual case safety report (ICSR) management and regulatory reporting timelinesRef: EMA GVP Module VI (Rev 2); ICH E2D(R1); ICH E2B(R3) | Expert |
Role-Specific Knowledge
Includes topics derived from this role's job description.
| Topic | Level |
|---|---|
| PV data ecosystem and FAIR data principlesRef: FAIR Guiding Principles (Wilkinson et al., 2016) | Expert |
| Statistical, machine-learning and NLP methods for safety data | Expert |
| Signal detection analytics and dashboardsRef: EMA GVP Module IX Addendum I | Intermediate |
| Data privacy and ethical use of dataRef: EU GDPR — Regulation (EU) 2016/679 | Intermediate |
| Signal management process (GVP Module IX)Ref: EMA GVP Module IX — Signal management (Rev 1) | Expert |
| Risk management planning (GVP Module V): RMP structure and lifecycleRef: EMA GVP Module V — Risk management systems (Rev 2) | Expert |
| Aggregate safety reports (PBRER/PSUR, DSUR)Ref: EMA GVP Module VII (PSUR); ICH E2C(R2) (PBRER); ICH E2F (DSUR) | Expert |
| Audits and regulatory inspectionsRef: EMA GVP Module IV — Pharmacovigilance audits (Rev 1); GVP Module III — Pharmacovigilance inspections | Expert |
| Safety database administration and configurationRef: ICH E2B(R3); EMA EudraVigilance | Expert |
| Training, education and capacity buildingRef: WHO Pharmacovigilance Core Curriculum for University Teaching | Intermediate |
| Vendor governance and outsourcing oversightRef: EMA GVP Module I (outsourced activities) | Expert |
| Benefit–risk assessment methodologyRef: ICH E2C(R2) — Periodic Benefit-Risk Evaluation Report (PBRER) | Expert |
| Biostatistics and quantitative safety analysis | Expert |
Knowledge · Skills · Attitude
A summary of the competency elements expected for this role.
| Element | Knowledge | Skills | Attitude |
|---|---|---|---|
| Analytics |
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| Data engineering |
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| Ethics |
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Get certified for this role
Certification validates prerequisites via CV screening and assesses knowledge through an online, AI-proctored examination based on the competency standard. The latest terms and requirements for every role and region are maintained by the Institute of Pharmacovigilance.
