Ammara ITH

Ammara ITH

Entrepreneurship project at INPG Engineering school

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  • Timeline

  • About me

    Digital Performance Insight Manager chez Hilti Group

  • Education

    • Scientific preparatory classes at Janson de Sailly, France, Paris

      2007 - 2008
    • Institut polytechnique de Grenoble

      2008 - 2012
      Engineer's degree Physics, computer science, probability and statistics
    • Kungliga tekniska högskolan

      2010 - 2011
      Royal Institute of Technology - Sweden, Stockholm - Master Leadership and management
  • Experience

    • Grenoble INP - National Polytechnic Institute of Grenoble

      Sept 2009 - May 2010
      Entrepreneurship project at INPG Engineering school

      Entrepreneurship project: preparing a business plan on face protection.

    • CGI (ex-Logica)

      Sept 2011 - Feb 2012
      Business Analyst

      Client: a gas utility company.Technical environment: SAP CRM (Customer Relationship Management) and SAP IS-U (billing).Responsabilities handled:- Acting as single point of contact between stakeholders and the project team- Analysing production anomalies- Developing reports using Visual Basic Advanced (VBA)- Conducting weekly meetings with the client- Capitalising process knowledge through documentation

    • Accenture

      Jan 2013 - Aug 2019

      Client: an electric utility company.Technical environment: Oracle CC&B (Customer Care and Billing), SQL developerResponsabilities handled:- Negotiating with the development team to drive delivery of maximum business value and meet the agreed costs- Drawing up and setting action plans in case of requirement or budgetary slippages - Coordinating the business analyst team with external teams- Controlling the design and requirement phase to ensure consistency of the portfolio and the respect of the other application plannings Show less Client: an electric utility company.Technical environment: Oracle CC&B (Customer Care and Billing), SQL developerResponsabilities handled:- Identifying and gathering business requirements through workshops with stakeholders- Analysing the information collected and preparing functional documentation on requirements- Reviewing and validating technical specifications- Training end-users in view of change management- Participating in the implemention of Agile methods in a traditional V-model application Show less

      • BI Consultant

        Feb 2017 - Aug 2019
      • Consultant

        Jan 2013 - Aug 2019
      • Data Scientist

        Jul 2016 - Dec 2016
      • Lead Business Analyst

        Sept 2015 - Dec 2016
      • Business Analyst

        Jan 2013 - Aug 2015
    • Kaggle

      Oct 2015 - Dec 2017
      Data science competitor

      Kaggle.com is a world-wide online platform hosting data science competitions.- Participating to pratice machine learning and data analysis skills on real datasets in an emulative world-wide environment- Competing with R language with random forest, SVM, multinomial models and using a bagging, boosting and ensemble techniques

    • Hilti Group

      Aug 2019 - now
      Digital Performance Insight Manager

      Business Intelligence & Data analysis & Analytics for engagement marketing and customer experience to analyze and optimize marketing campaigns:- Building KPIs and dashboards to monitor digital and physical performance,- Providing analytical support and insights to business stakeholders and leaders for data driven decisions,- Capitalizing knowledge and best practices on BI tools globally.Power BI, SQL, Google Analytics, Google Big Query, Microsoft Azure, Amazon Web Services (Redshift) Show less

  • Licenses & Certifications

    • Certified Professional for Requirements Engineering (REQB Foundation Level)

      REQB.org
      Jun 2015
    • Certified ScrumMaster

      Scrum Alliance
      Dec 2015
      View certificate certificate
    • Machine Learning - Stanford University

      Coursera
    • R programming, Getting and cleaning data, Regression models, Exploratory analysis - Johns Hopkins University

      Coursera
      May 2015
    • Data analysis and statistical inferences - Duke University

      Coursera