Tom Guest

Tom Guest

About

Detail

Head of Product Operations
London, England, United Kingdom

Timeline

work
Job
school
Education
folder
Project

Résumé

Jobs verified_user 0% verified
  • TrustedHousesitters
    Head of Product Operations
    TrustedHousesitters
    Jan 2025 - Current (1 year 10 months)
  • IVC Evidensia
    Agile Practice Lead
    IVC Evidensia
    Sep 2023 - Jan 2025 (1 year 5 months)
  • Cazoo
    Agile Coach
    Cazoo
    Aug 2019 - Jun 2023 (3 years 11 months)
    Nov 2021 - Jun 2023: Lead Agile Coach

    Aug 2019 - Nov 2021: Agile Coach
  • Sky
    Agile Coach
    Sky
    Dec 2017 - Aug 2019 (1 year 9 months)
  • Sainsburys
    Agile Coach
    Sainsburys
    Jun 2017 - Nov 2017 (6 months)
  • Moonpigcom
    Scrum Master
    Moonpigcom
    Dec 2016 - Jun 2017 (7 months)
  • Sainsburys
    Agile Coach
    Sainsburys
    Jun 2016 - Dec 2016 (7 months)
  • Direct Line Group
    Scrum Master
    Direct Line Group
    Feb 2016 - Jun 2016 (5 months)
  • Which
    Scrum Master
    Which
    Sep 2015 - Dec 2015 (4 months)
  • CACI Ltd
    Scrum Master
    CACI Ltd
    Feb 2015 - Jun 2015 (5 months)
  • M
    Senior Scrum Master
    Macmillan Science and Education
    Aug 2014 - Dec 2014 (5 months)
  • M
    Scrum Master
    Macmillan Science and Education
    Jun 2013 - Aug 2014 (1 year 3 months)
  • RMS
    Scrum Master
    RMS
    Jun 2012 - Jun 2013 (1 year 1 month)
  • RMS
    Analyst
    RMS
    Oct 2010 - Jun 2012 (1 year 9 months)
  • Schlumberger
    Internship
    Schlumberger
    Sep 2008 - Dec 2008 (4 months)
  • University of Edinburgh
    PhD Student
    University of Edinburgh
    Oct 2006 - Nov 2010 (4 years 2 months)
    Nonlinear Design of Geophysical Surveys and Processing Strategies
  • C
    Consultant
    Cambridge Environmental Research Consultants
    Sep 2006 - Sep 2007 (1 year 1 month)
Education verified_user 0% verified
  • The University of Edinburgh
    Doctor of Philosophy (PhD, Geophysics and Seismology
    The University of Edinburgh
    Jan 2006 - Jan 2010 (4 years 1 month)
  • University of Cambridge
    MPhil, Physics
    University of Cambridge
    Jan 2004 - Jan 2005 (1 year 1 month)
  • Penn State University
    MGeophys, Geophysics
    Penn State University
    Jan 2002 - Jan 2003 (1 year 1 month)
  • University of Leeds
    MGeophys, Geophysics
    University of Leeds
    Jan 2000 - Jan 2004 (4 years 1 month)
Projects (professional or personal) verified_user 0% verified
    Publications verified_user 0% verified
    • G
      On standard and optimal designs of industrial-scale 2-D seismic surveys
      Geophysical Journal International Jun
      The principal aim of performing a survey or experiment is to maximize the desired information within a data set by minimizing the post-survey uncertainty on the ranges of the model parameter values. Using Bayesian, non-linear, statistical experimental design (SED) methods we show how industrial scale amplitude variations with offset (AVO) surveys can be constructed to maximize the information content contained in AVO crossplots, the principal source of petrophysical information from seismic surveys. The design method allows offset dependent errors, previously not allowed in non-linear geoscientific SED methods. The method is applied to a single common-midpoint gather. The results show that the optimal design is highly dependent on the range
    • J
      Iteratively constructive sequential design of experiments and surveys with nonlinear parameter-data relationships
      JOURNAL OF GEOPHYSICAL RESEARCH Apr
      In experimental design, the main aim is to minimize post experimental uncertainty on parameters by maximizing relevant information collected in a data set. Using an entropy-based method constructed on a Bayesian framework, it is possible to design experiments for highly nonlinear problems. However, the method is computationally infeasible for design spaces with even a few dimensions. We introduce an iteratively constructive method that reduces the computational demand by introducing one new datum at a time for the design. The method reduces the multidimensional design space to a single-dimensional space at each iteration by fixing the experimental setup of the previous iteration. Both a synthetic experiment using a highly nonlinear paramete
    • G
      Optimal Trace Selection for Amplitude Versus Angle (AVA) Processing of Shale-Sand Reservoirs
      Geophysics Jul
      When processing data, a principal aim is to maximize information inferred from a data set by minimizing the expected postprocessing uncertainties on parameters of interest. Nonlinear statistical experimental design ?SED? methods can be used to find optimal trace profiles for processing amplitude-variation-with-angle ?AVA? surveys that account for all prior petrophysical information about the target reservoir. Optimal selections change as prior knowledge of rock parameters and reservoir fluid content changes, and which of the prior parameters have the greatest effect on selected traces can be assessed. The results show that optimal profiles are far more sensitive to prior information about reservoir porosity than to information about satur
    This is a community-created genome.