Chaima Boughanmi
Data Scientist @ BVA Xsight | R shiny developer
Organizer @ R-Ladies Paris
Paris France
BVA Xsight
R-Ladies Paris
Biography
Chaima BOUGHANMI is a Data Scientist at BVA Xsight in Paris. Data science was the perfectly suited discipline for her. She is an engineer in statistics and information analysis.
She holds a master’s degree in data science from Université Paris Saclay and a research master’s degree in information processing and complexity of living systems.
Very comfortable with numbers and statistics, she likes to give them meaning and see them have an impact on reality.
She has worked in several fields: energy, sports, polling, FMCG, automotive, telecom, media and marketing.
During the current missions, she specializes in Marketing Mix Modeling (MMM) and ROI market research, with a focus on quantifying the effects of marketing decisions and estimating the value of intangible assets through 3-dimensional modeling—mathematical, financial, and visual. Her expertise includes implementing Machine Learning (ML) models such as Random Forest and XGBoost to solve customer issues, along with developing attribution models using Markov chains. She creates interactive dashboards and graphs with Shiny to enhance data visualization. Additionally, She has experience in synergy calculation methods for multiplicative models within MMM and in estimating Bayesian models using PySTAN and Markov Chain Monte Carlo (MCMC) methods.
During her studies, Ms. Boughanmi acquired experience in mathematical modeling, data analysis, programming, data visualization and Machine Learning.
She also had the opportunity, through her engineering internship, her two end-of-study internships for master’s studies and academic projects, to gain an understanding of the data analysis issues in which she was particularly interested, and to build up experience in this field.
Through her studies, Chaima developed a deep interest in large-scale data analysis and processing, forecasting, optimization, data mining and graph theory.
She loves bringing numbers to life, giving them meaning and value.
She has a very good mastery of programming languages, especially
R and R/shiny. She was a member of R-Ladies Tunis when she was a student and is currently one of the organizers of R-Ladies Paris.
She also masters Python, SQL, SAS and obviously Latex and Git.
Miss Boughanmi is a footballer, so when she’s not playing with numbers and data, she’s playing with the ball ! That’s why she chose to work on tactical analysis of team sports during her Master’s research at GREYC laboratory.
With the Paris Saclay team, Chaima and her teammates qualified for the 2022 france championship, finishing first in île de France and 5th in all of France.
- Statistics & Machine learning
- Coding
- Data analysis and visualization
- Marketing Mix Modeling (MMM)
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Master's degree in Data Science, 2022
Université Paris Scalay
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Engineering diploma in statistics and information analysis, 2021
Ecole supérieure de la statistique et de l'analyse de l'information
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Master's degree in information processing and complexity of living systems, 2021
Ecole d'igénieurs de Tunis
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National competitive engineering entrance examination, 2018
Preparatory institute for engineering studies
Skills
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Experiences
- Marketing Mix Modeling : Analysing Marketing Mix Modeling, ROI Market Research, quantify the past and future effects of decisions, estimate the value of intangible assets. 3-dimensional modeling: mathematical to connect all the factors that explain business performance, financial to arbitrate, and visual to collaborate and mediate.
- Implementation of Machine Learning models to solve customer issues : Attribution models (use of Random Forest, One Hot Encoding with hyperparameter optimization using XGBoost - Cross Validation and class balancing techniques).
- Implementing attribution models with Markov chains.
- Development of interactive graphs and dashboards with shiny.
- Exploring and implementing synergy calculation methods for a multiplicative model within the context of a Marketing Mix: Application to our internal data.
- Implementation of a PySTAN model using Markov Chains Monte Carlo (MCMC) to estimate Bayesian models.
Technology : R - Shniy - Python - MassTer - Marketing Mix Modeling - Econometry - Statistical Modelisation - Random Forest - XGBoost - Markov Chains - Bayesian Modeling
Fields : FMCG - Telecommunication - Automotive sector
- Give talks and presentations on an R topic.
- Help organize conferences and workshops using the R language.
- Make posters, communicate and publish our events and materials (recordings, code, slides) on our social networks.
- Contact and host speakers and trainers to promote the use of the R language worldwide.
Data Science applied to the planning process: creating value through exploration and analysis of brute data:
- Creation of SQL queries to extract data from various department databases.
- Processing and analysis of data required to improve the ‘planning process’.
- Creation, publication and documentation of an interactive dashboard using R Shiny.
- Classification of ’energy works’ and accuracy of consumption using Machine Learning algorithms with python.
- Documentation using Rmarkdown and Git.
Technology : R - Shiny - Rmarkdown - SQL - Python - Git - Machine Learning - Time series
Fields : Energy sector
- Animating the practical part of the training session for managers of the National Road Safety Observatory.
- Application using R, descriptive statistics and inferential statistics.
Technology : R - Descriptive & inferential statistical methods
Fields : Health sector
- Tactical analysis of team sports data : Implementation of an algorithm, using Object Oriented Programming (OOP) with Python, based on dynamic graphs to identify styles of play by detecting frequent patterns among players in collective sports.
- Applying the algorithm to data from the Qatar 2015 handball world cup.
- Participation in the editing of a research article using Latex.
Technology: Python - Object Oriented Programming (OOP) - Dynamic graphs - Tactical analysis - Latex
Fields : Sport sector
- Help organize conferences and workshops using the R language.
- Make posters, communicate and publish our events and materials (recordings, code, slides) on our social networks.
- Contact and host speakers and trainers to promote the use of the R language worldwide.
KAP survey on early childhood in Tunisia: In partnership with UNICEF, Tunisia resorted to conducting a KAP survey for parenting education (2018) in connection with the National Multisectoral Strategy for Early Childhood Development. My missions were:
- Evaluation and analysis of Tunisian parents’ knowledge and practices regarding nutrition, health and the education of young children.
- Analyze data and create explanatory graphs.
- Calculation of indicators and implementation of R shiny dashboards.
Technology : R - shiny - Statistics - Data analysis
Fields : Social sector
Accomplishments
Projects
