Pubblicato su stage4eu il: 28/08/2019 General Electric, HR People Analytics Intern

General Electric
The Ark, 201 Talgarth Rd, Hammersmith, London, Regno Unito
Organizzazione e Gestione Risorse umane, Statistica/Data analysis
12 mesi 
Posti disponibili Non specificato
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  • Support HR Leadership team with people related data driven insights taken from statistical analyses of HR data sets. 
  • Summarise and provide graphical visualisations on key people metrics such as diversity, attrition, promotions and more. 
  • Lead on development of key algorithms around attrition, diversity, culture, and other important people metrics. 
  • Partner with HR leaders on designing data research, defining hypotheses, data collection and running multiple different statistical models. 
  • Effectively use HR reporting tools and dashboards to provide ad hoc support to HR leadership team.
  • Assist in development on future of work steering committee and educational material for HR team. 
  • Assisting in monitoring of data quality and holding HRMs accountable with their data ownership.
Requisiti principali:
  • Bachelor, Masters, or Ph.D. in data science, mathematics, statistics, computer science, economics or related scientific field of study.
  • Coursework in statistics and application of statistical tools.
  • Working knowledge of Tableau
  • Fluency in English, with polite, professional communication skills.
  • Intermediate to advanced knowledge of applied statistical techniques and statistical packages, i.e. R, Python, SPSS or other.
  • Ability to prioritise multiple tasks & work to deadlines.

Desired Characteristics:

  • Previous experience where you’ve proven success in a highly professional Customer Operations or HR role, ideally within a multinational organisation is an advantage.
  • Proficient in MS Office Suite with expert skills in MS Excel (Macros, Pivot Tables), Access, and PowerPoint.
  • Experience with R or Python an advantage.
  • Superior analytical skills with the ability to assess data and trends using both quantitative and qualitative analysis techniques.