Past PSI Events

Conferences

Connecting the False Discovery Rate to Shrunk Estimates

Date: Tuesday 5th May 2026
Time: 14:00 - 15:00 GMT | 9:00 - 10:00 EST (US)  | 15:00 - 16:00 CET
Location: Online via Zoom
Speakers: Nick Galwey, GSK (Retired)

Who is this event intended for?: 
Statisticians interested in learning about False Discovery Rate and Shrunk Estimates
 
What is the benefit of attending?:
To gain a deeper understanding of tools used to address multiplicity, explore how these methods relate to one another, and see how they perform when applied to both real human gene‑expression data and simulated datasets.

Brief event overview:
This talk will explore the “replication crisis” in science, focusing on how testing large numbers of hypotheses can lead to false positive findings. It introduces key statistical approaches—False Discovery Rate (FDR) and shrinkage methods—to address this issue, and explains their conceptual foundations and connections. The session will also highlight how these tools can be understood within an empirical-Bayesian framework, linking significance testing with effect size estimation.

Cost:
This webinar is free to both Members of PSI and Non-Members.

Registration:
To register for this event, please click here

Overview:

Science is currently facing a ‘replication crisis’ – a concern that many scientific findings reported are difficult or impossible to reproduce.   A major cause of this is the availability of technology that permits the exploration and testing of very large numbers of hypotheses, some of which will almost certainly show significant or large effects by chance, even when no real effects are present: this is the ‘multiplicity’ or ‘multiple testing’ problem.   The statistical tools available to address this problem include:

  • the False Discovery Rate (FDR), which is specified in relation to the subset of the m hypotheses tested for which the discovery of an effect is reported, and which indicates the proportion of these ‘discoveries’ that is expected to be false; and
  • shrunk estimates, which reduce the estimated effect, in relation to every individual hypothesis, from the observed value towards the null value.

This talk will first examine the conceptual basis for each of these tools, then consider how they are connected.   Though the FDR and shrunk estimates are both conventionally presented in the frequentist statistical framework, they can both also be presented in empirical-Bayesian terms, the prior probability distribution being calculated from the data relating to the m hypotheses tested, as follows:

  • in the case of the FDR, from the proportion of the m significance tests conducted that give a p-value at or below the specified significance threshold, and that are therefore announced as ‘discoveries’; and
  • in the case of shrunk estimates, from the distribution of the observed effect sizes over the m hypotheses.
Based on this connection, a formal relationship between FDR values and shrunk estimates will be presented, and it will be argued that these two tools can profitably be used in conjunction.   Their combined application, both to real (human gene expression) data and to simulated data, will be illustrated.   

Speaker details

Speaker

Biography

Nick Galwey, Former Statistics Leader, GSK (retired)

Nick was a demonstrator and lecturer in biometry and plant breeding at the University of Cambridge from 1979 to 1995, when he was appointed to a senior lectureship at the University of Western Australia, Perth.   In 2001 he returned to the UK, working as a statistical geneticist at Oxagen Limited, Oxfordshire, until 2004. He then moved to GlaxoSmithKline, first as a statistical geneticist and later as a statistician, supporting pre clinical and clinical research and epidemiology, until he retired in September 2024.   He is the author, or a co-author, of more than 100 scientific papers.  His most recent book is:
Galwey, N.W. (2025) The False Discovery Rate: Its Meaning, Interpretation and Application in Data Science. Chichester, UK: Wiley. 266pp. ISBN 9781119889779 



Scientific Meetings

Connecting the False Discovery Rate to Shrunk Estimates

Date: Tuesday 5th May 2026
Time: 14:00 - 15:00 GMT | 9:00 - 10:00 EST (US)  | 15:00 - 16:00 CET
Location: Online via Zoom
Speakers: Nick Galwey, GSK (Retired)

Who is this event intended for?: 
Statisticians interested in learning about False Discovery Rate and Shrunk Estimates
 
What is the benefit of attending?:
To gain a deeper understanding of tools used to address multiplicity, explore how these methods relate to one another, and see how they perform when applied to both real human gene‑expression data and simulated datasets.

Brief event overview:
This talk will explore the “replication crisis” in science, focusing on how testing large numbers of hypotheses can lead to false positive findings. It introduces key statistical approaches—False Discovery Rate (FDR) and shrinkage methods—to address this issue, and explains their conceptual foundations and connections. The session will also highlight how these tools can be understood within an empirical-Bayesian framework, linking significance testing with effect size estimation.

Cost:
This webinar is free to both Members of PSI and Non-Members.

Registration:
To register for this event, please click here

Overview:

Science is currently facing a ‘replication crisis’ – a concern that many scientific findings reported are difficult or impossible to reproduce.   A major cause of this is the availability of technology that permits the exploration and testing of very large numbers of hypotheses, some of which will almost certainly show significant or large effects by chance, even when no real effects are present: this is the ‘multiplicity’ or ‘multiple testing’ problem.   The statistical tools available to address this problem include:

  • the False Discovery Rate (FDR), which is specified in relation to the subset of the m hypotheses tested for which the discovery of an effect is reported, and which indicates the proportion of these ‘discoveries’ that is expected to be false; and
  • shrunk estimates, which reduce the estimated effect, in relation to every individual hypothesis, from the observed value towards the null value.

This talk will first examine the conceptual basis for each of these tools, then consider how they are connected.   Though the FDR and shrunk estimates are both conventionally presented in the frequentist statistical framework, they can both also be presented in empirical-Bayesian terms, the prior probability distribution being calculated from the data relating to the m hypotheses tested, as follows:

  • in the case of the FDR, from the proportion of the m significance tests conducted that give a p-value at or below the specified significance threshold, and that are therefore announced as ‘discoveries’; and
  • in the case of shrunk estimates, from the distribution of the observed effect sizes over the m hypotheses.
Based on this connection, a formal relationship between FDR values and shrunk estimates will be presented, and it will be argued that these two tools can profitably be used in conjunction.   Their combined application, both to real (human gene expression) data and to simulated data, will be illustrated.   

Speaker details

Speaker

Biography

Nick Galwey, Former Statistics Leader, GSK (retired)

Nick was a demonstrator and lecturer in biometry and plant breeding at the University of Cambridge from 1979 to 1995, when he was appointed to a senior lectureship at the University of Western Australia, Perth.   In 2001 he returned to the UK, working as a statistical geneticist at Oxagen Limited, Oxfordshire, until 2004. He then moved to GlaxoSmithKline, first as a statistical geneticist and later as a statistician, supporting pre clinical and clinical research and epidemiology, until he retired in September 2024.   He is the author, or a co-author, of more than 100 scientific papers.  His most recent book is:
Galwey, N.W. (2025) The False Discovery Rate: Its Meaning, Interpretation and Application in Data Science. Chichester, UK: Wiley. 266pp. ISBN 9781119889779 



Training Courses

Connecting the False Discovery Rate to Shrunk Estimates

Date: Tuesday 5th May 2026
Time: 14:00 - 15:00 GMT | 9:00 - 10:00 EST (US)  | 15:00 - 16:00 CET
Location: Online via Zoom
Speakers: Nick Galwey, GSK (Retired)

Who is this event intended for?: 
Statisticians interested in learning about False Discovery Rate and Shrunk Estimates
 
What is the benefit of attending?:
To gain a deeper understanding of tools used to address multiplicity, explore how these methods relate to one another, and see how they perform when applied to both real human gene‑expression data and simulated datasets.

Brief event overview:
This talk will explore the “replication crisis” in science, focusing on how testing large numbers of hypotheses can lead to false positive findings. It introduces key statistical approaches—False Discovery Rate (FDR) and shrinkage methods—to address this issue, and explains their conceptual foundations and connections. The session will also highlight how these tools can be understood within an empirical-Bayesian framework, linking significance testing with effect size estimation.

Cost:
This webinar is free to both Members of PSI and Non-Members.

Registration:
To register for this event, please click here

Overview:

Science is currently facing a ‘replication crisis’ – a concern that many scientific findings reported are difficult or impossible to reproduce.   A major cause of this is the availability of technology that permits the exploration and testing of very large numbers of hypotheses, some of which will almost certainly show significant or large effects by chance, even when no real effects are present: this is the ‘multiplicity’ or ‘multiple testing’ problem.   The statistical tools available to address this problem include:

  • the False Discovery Rate (FDR), which is specified in relation to the subset of the m hypotheses tested for which the discovery of an effect is reported, and which indicates the proportion of these ‘discoveries’ that is expected to be false; and
  • shrunk estimates, which reduce the estimated effect, in relation to every individual hypothesis, from the observed value towards the null value.

This talk will first examine the conceptual basis for each of these tools, then consider how they are connected.   Though the FDR and shrunk estimates are both conventionally presented in the frequentist statistical framework, they can both also be presented in empirical-Bayesian terms, the prior probability distribution being calculated from the data relating to the m hypotheses tested, as follows:

  • in the case of the FDR, from the proportion of the m significance tests conducted that give a p-value at or below the specified significance threshold, and that are therefore announced as ‘discoveries’; and
  • in the case of shrunk estimates, from the distribution of the observed effect sizes over the m hypotheses.
Based on this connection, a formal relationship between FDR values and shrunk estimates will be presented, and it will be argued that these two tools can profitably be used in conjunction.   Their combined application, both to real (human gene expression) data and to simulated data, will be illustrated.   

Speaker details

Speaker

Biography

Nick Galwey, Former Statistics Leader, GSK (retired)

Nick was a demonstrator and lecturer in biometry and plant breeding at the University of Cambridge from 1979 to 1995, when he was appointed to a senior lectureship at the University of Western Australia, Perth.   In 2001 he returned to the UK, working as a statistical geneticist at Oxagen Limited, Oxfordshire, until 2004. He then moved to GlaxoSmithKline, first as a statistical geneticist and later as a statistician, supporting pre clinical and clinical research and epidemiology, until he retired in September 2024.   He is the author, or a co-author, of more than 100 scientific papers.  His most recent book is:
Galwey, N.W. (2025) The False Discovery Rate: Its Meaning, Interpretation and Application in Data Science. Chichester, UK: Wiley. 266pp. ISBN 9781119889779 



Journal Club

Connecting the False Discovery Rate to Shrunk Estimates

Date: Tuesday 5th May 2026
Time: 14:00 - 15:00 GMT | 9:00 - 10:00 EST (US)  | 15:00 - 16:00 CET
Location: Online via Zoom
Speakers: Nick Galwey, GSK (Retired)

Who is this event intended for?: 
Statisticians interested in learning about False Discovery Rate and Shrunk Estimates
 
What is the benefit of attending?:
To gain a deeper understanding of tools used to address multiplicity, explore how these methods relate to one another, and see how they perform when applied to both real human gene‑expression data and simulated datasets.

Brief event overview:
This talk will explore the “replication crisis” in science, focusing on how testing large numbers of hypotheses can lead to false positive findings. It introduces key statistical approaches—False Discovery Rate (FDR) and shrinkage methods—to address this issue, and explains their conceptual foundations and connections. The session will also highlight how these tools can be understood within an empirical-Bayesian framework, linking significance testing with effect size estimation.

Cost:
This webinar is free to both Members of PSI and Non-Members.

Registration:
To register for this event, please click here

Overview:

Science is currently facing a ‘replication crisis’ – a concern that many scientific findings reported are difficult or impossible to reproduce.   A major cause of this is the availability of technology that permits the exploration and testing of very large numbers of hypotheses, some of which will almost certainly show significant or large effects by chance, even when no real effects are present: this is the ‘multiplicity’ or ‘multiple testing’ problem.   The statistical tools available to address this problem include:

  • the False Discovery Rate (FDR), which is specified in relation to the subset of the m hypotheses tested for which the discovery of an effect is reported, and which indicates the proportion of these ‘discoveries’ that is expected to be false; and
  • shrunk estimates, which reduce the estimated effect, in relation to every individual hypothesis, from the observed value towards the null value.

This talk will first examine the conceptual basis for each of these tools, then consider how they are connected.   Though the FDR and shrunk estimates are both conventionally presented in the frequentist statistical framework, they can both also be presented in empirical-Bayesian terms, the prior probability distribution being calculated from the data relating to the m hypotheses tested, as follows:

  • in the case of the FDR, from the proportion of the m significance tests conducted that give a p-value at or below the specified significance threshold, and that are therefore announced as ‘discoveries’; and
  • in the case of shrunk estimates, from the distribution of the observed effect sizes over the m hypotheses.
Based on this connection, a formal relationship between FDR values and shrunk estimates will be presented, and it will be argued that these two tools can profitably be used in conjunction.   Their combined application, both to real (human gene expression) data and to simulated data, will be illustrated.   

Speaker details

Speaker

Biography

Nick Galwey, Former Statistics Leader, GSK (retired)

Nick was a demonstrator and lecturer in biometry and plant breeding at the University of Cambridge from 1979 to 1995, when he was appointed to a senior lectureship at the University of Western Australia, Perth.   In 2001 he returned to the UK, working as a statistical geneticist at Oxagen Limited, Oxfordshire, until 2004. He then moved to GlaxoSmithKline, first as a statistical geneticist and later as a statistician, supporting pre clinical and clinical research and epidemiology, until he retired in September 2024.   He is the author, or a co-author, of more than 100 scientific papers.  His most recent book is:
Galwey, N.W. (2025) The False Discovery Rate: Its Meaning, Interpretation and Application in Data Science. Chichester, UK: Wiley. 266pp. ISBN 9781119889779 



Webinars

Connecting the False Discovery Rate to Shrunk Estimates

Date: Tuesday 5th May 2026
Time: 14:00 - 15:00 GMT | 9:00 - 10:00 EST (US)  | 15:00 - 16:00 CET
Location: Online via Zoom
Speakers: Nick Galwey, GSK (Retired)

Who is this event intended for?: 
Statisticians interested in learning about False Discovery Rate and Shrunk Estimates
 
What is the benefit of attending?:
To gain a deeper understanding of tools used to address multiplicity, explore how these methods relate to one another, and see how they perform when applied to both real human gene‑expression data and simulated datasets.

Brief event overview:
This talk will explore the “replication crisis” in science, focusing on how testing large numbers of hypotheses can lead to false positive findings. It introduces key statistical approaches—False Discovery Rate (FDR) and shrinkage methods—to address this issue, and explains their conceptual foundations and connections. The session will also highlight how these tools can be understood within an empirical-Bayesian framework, linking significance testing with effect size estimation.

Cost:
This webinar is free to both Members of PSI and Non-Members.

Registration:
To register for this event, please click here

Overview:

Science is currently facing a ‘replication crisis’ – a concern that many scientific findings reported are difficult or impossible to reproduce.   A major cause of this is the availability of technology that permits the exploration and testing of very large numbers of hypotheses, some of which will almost certainly show significant or large effects by chance, even when no real effects are present: this is the ‘multiplicity’ or ‘multiple testing’ problem.   The statistical tools available to address this problem include:

  • the False Discovery Rate (FDR), which is specified in relation to the subset of the m hypotheses tested for which the discovery of an effect is reported, and which indicates the proportion of these ‘discoveries’ that is expected to be false; and
  • shrunk estimates, which reduce the estimated effect, in relation to every individual hypothesis, from the observed value towards the null value.

This talk will first examine the conceptual basis for each of these tools, then consider how they are connected.   Though the FDR and shrunk estimates are both conventionally presented in the frequentist statistical framework, they can both also be presented in empirical-Bayesian terms, the prior probability distribution being calculated from the data relating to the m hypotheses tested, as follows:

  • in the case of the FDR, from the proportion of the m significance tests conducted that give a p-value at or below the specified significance threshold, and that are therefore announced as ‘discoveries’; and
  • in the case of shrunk estimates, from the distribution of the observed effect sizes over the m hypotheses.
Based on this connection, a formal relationship between FDR values and shrunk estimates will be presented, and it will be argued that these two tools can profitably be used in conjunction.   Their combined application, both to real (human gene expression) data and to simulated data, will be illustrated.   

Speaker details

Speaker

Biography

Nick Galwey, Former Statistics Leader, GSK (retired)

Nick was a demonstrator and lecturer in biometry and plant breeding at the University of Cambridge from 1979 to 1995, when he was appointed to a senior lectureship at the University of Western Australia, Perth.   In 2001 he returned to the UK, working as a statistical geneticist at Oxagen Limited, Oxfordshire, until 2004. He then moved to GlaxoSmithKline, first as a statistical geneticist and later as a statistician, supporting pre clinical and clinical research and epidemiology, until he retired in September 2024.   He is the author, or a co-author, of more than 100 scientific papers.  His most recent book is:
Galwey, N.W. (2025) The False Discovery Rate: Its Meaning, Interpretation and Application in Data Science. Chichester, UK: Wiley. 266pp. ISBN 9781119889779 



Careers Meetings

Connecting the False Discovery Rate to Shrunk Estimates

Date: Tuesday 5th May 2026
Time: 14:00 - 15:00 GMT | 9:00 - 10:00 EST (US)  | 15:00 - 16:00 CET
Location: Online via Zoom
Speakers: Nick Galwey, GSK (Retired)

Who is this event intended for?: 
Statisticians interested in learning about False Discovery Rate and Shrunk Estimates
 
What is the benefit of attending?:
To gain a deeper understanding of tools used to address multiplicity, explore how these methods relate to one another, and see how they perform when applied to both real human gene‑expression data and simulated datasets.

Brief event overview:
This talk will explore the “replication crisis” in science, focusing on how testing large numbers of hypotheses can lead to false positive findings. It introduces key statistical approaches—False Discovery Rate (FDR) and shrinkage methods—to address this issue, and explains their conceptual foundations and connections. The session will also highlight how these tools can be understood within an empirical-Bayesian framework, linking significance testing with effect size estimation.

Cost:
This webinar is free to both Members of PSI and Non-Members.

Registration:
To register for this event, please click here

Overview:

Science is currently facing a ‘replication crisis’ – a concern that many scientific findings reported are difficult or impossible to reproduce.   A major cause of this is the availability of technology that permits the exploration and testing of very large numbers of hypotheses, some of which will almost certainly show significant or large effects by chance, even when no real effects are present: this is the ‘multiplicity’ or ‘multiple testing’ problem.   The statistical tools available to address this problem include:

  • the False Discovery Rate (FDR), which is specified in relation to the subset of the m hypotheses tested for which the discovery of an effect is reported, and which indicates the proportion of these ‘discoveries’ that is expected to be false; and
  • shrunk estimates, which reduce the estimated effect, in relation to every individual hypothesis, from the observed value towards the null value.

This talk will first examine the conceptual basis for each of these tools, then consider how they are connected.   Though the FDR and shrunk estimates are both conventionally presented in the frequentist statistical framework, they can both also be presented in empirical-Bayesian terms, the prior probability distribution being calculated from the data relating to the m hypotheses tested, as follows:

  • in the case of the FDR, from the proportion of the m significance tests conducted that give a p-value at or below the specified significance threshold, and that are therefore announced as ‘discoveries’; and
  • in the case of shrunk estimates, from the distribution of the observed effect sizes over the m hypotheses.
Based on this connection, a formal relationship between FDR values and shrunk estimates will be presented, and it will be argued that these two tools can profitably be used in conjunction.   Their combined application, both to real (human gene expression) data and to simulated data, will be illustrated.   

Speaker details

Speaker

Biography

Nick Galwey, Former Statistics Leader, GSK (retired)

Nick was a demonstrator and lecturer in biometry and plant breeding at the University of Cambridge from 1979 to 1995, when he was appointed to a senior lectureship at the University of Western Australia, Perth.   In 2001 he returned to the UK, working as a statistical geneticist at Oxagen Limited, Oxfordshire, until 2004. He then moved to GlaxoSmithKline, first as a statistical geneticist and later as a statistician, supporting pre clinical and clinical research and epidemiology, until he retired in September 2024.   He is the author, or a co-author, of more than 100 scientific papers.  His most recent book is:
Galwey, N.W. (2025) The False Discovery Rate: Its Meaning, Interpretation and Application in Data Science. Chichester, UK: Wiley. 266pp. ISBN 9781119889779 



Upcoming Events

PSI Introduction to Industry Training (ITIT) Course - 2026/2027

An introductory course giving an overview of the pharmaceutical industry and the drug development process as a whole, aimed at those with 1-3 years' experience. It comprises of six 2-day sessions covering a range of topics including Research and Development, Toxicology, Data Management and the Role of a CRO, Clinical Trials, Reimbursement, and Marketing.

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Our monthly webinar explores examples of innovative data visualisations relevant to our day to day work. Each month a new dataset is provided from a clinical trial or other relevant example, and participants are invited to submit a graphic that communicates interesting and relevant characteristics of the data.

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This is a Book Club Special Event in response to the changes in our industry and as a supportive move to create community and connection for those navigating redundancy and uncertainty. Read the book in advance of the book club session then join the zoom call to discuss ideas. There will be breakout groups to connect with others, exchange experiences of how the book has helped, and offer support.

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Organizations have to adapt to the transforming landscape of our industry to ensure they continue to be successful in the future. Many of us are feeling the impact of organizational change. By reading John P Kotter’s book we can understand about organizational change and learn how to thrive, rather than just survive, through change. Change, by John P Kotter (and his team), is a summary of all that he has learned over his decades of research and leading change. His book describes why many current approaches to change are inadequate and explains why new solutions need to give people a voice and a role in a new, change-embracing organization. Develop your understanding of organisational change and become empowered to be part of your organisation’s change, by reading Change by John P Kotter and joining the Sept-Dec 2025 book club. You will be invited to join facilitated discussions of the concepts and ideas and apply knowledge from the book in-between sessions.

PSI Career Young Virtual Event (Q3 2026)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

PSI Career Young Virtual Event (Q4 2026)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

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