Amel Mhamdi
Data scientist
38 years old
Driving License
Professional Status
Employed
Available
About Me
Experte intelligence artificielle diplômée d'un Doctorat en Intelligence Artificielle en 2016, je me suis spécialisée sur l'analyse des données ainsi que l'automatisation des processus de travail et la prédiction via les Framework de l'IA et les algorithmes de Machine Learning.
Éternelle optimiste et curieuse de nature, je m'intéresse à tout ce qui me passe sous la main.
J'aime apprendre de nouveaux métiers et outils liés au Data, IA et le cloud et je suis toujours prête à aider mon équipe pour atteindre nos objectifs !
Éternelle optimiste et curieuse de nature, je m'intéresse à tout ce qui me passe sous la main.
J'aime apprendre de nouveaux métiers et outils liés au Data, IA et le cloud et je suis toujours prête à aider mon équipe pour atteindre nos objectifs !
Experiences
Responsable projet Intelligence artificielle chez France Télévision Via Wewyse
France Television
Since July 2019
- Contribution
Cartographie et mise en place des projets IA pour le domaine de Media - Responsabilités :
• Participation à la préparation d’organigramme de l’unité d’intelligence artificielle transverse de France tv : DaIA
• Définition des cas d’usage pour la définition des projets IA rapide au déploiement.
Mise en place de la méthode agile aux seins de l’équipe.
• Benchmarking des solutions de marché pour la transcription (Speech To Text)
• Mise en place d’un corpus Ftv pour un service de Speech To Text adapté en différents accents françaises, ainsi qu’un outil de transcription (python : speech-recognition).
• Mise en place d’un service de traitement de langage naturelle (NLP) (python : Spacy).
• Encadrement d’une équipe de six étudiant de l’ECE pour la mise en place d’un outil automatique de dépouillement des scénarios (python : Django).
• Visualisation des données des débats d’élection municipale dans les différentes régions et à une échelle nationale.
• La coopération avec l’équipe Infrastructure pour définir les besoins en termes de services sur le Cloud AWS.
• Recueille directe des besoins d’utilisateur (rédaction, indexation, journaliste, Infographistes).
• Coopération avec l’EBU pour la mise en place d’outils media européen pour le Benchmarking des solutions Speech To Text et la mise en place d’une data Lake coopératives pour le test des différents algorithmes de machine Learning.
• Création d’appel d’offre et recrutement des profils data ingénieur
• Communication des résultats de recherche à des conférences internationaux. - Environnement Technique : Python, Postgree, Kibana, elasticsearch, Git, Docker, Jira, Confluence
Data Consultant
Edenred
Since September 2018
- Context:
As part of the digital transformation of the IS department of Edenred, I joined the team of Edenred Connect, responsible for the SOO and communication with the various APIs of the IS (CRM ...).
The application manages the authentication and transactions of the company's web services, employees and merchants, whether by card, mobile or on online platforms.
Team:
1 scrum master, 2 in, 5 developers
Project organisation:
Agile (Sprints of a week) - Contribution:
In charge of customer data, their use, their provision and their exploitation for the prediction of the fraudulent.
Set up a support automation tool: enable customers to collect problem solutions and get expert advice / knowledge about problems via chatBot, without having to call an agent support team. - Mission:
- Collection of need in close collaboration with the profession
- Management and administration of the database for Edenred-Connect
- Versioning Management under Azure DevOps
- Prepare data analysis in Azure machine learning and export from Azure machine learning
- Develop machine learning models
- Operationalize and manage Azure machine learning services
- Use other services for machine learning
- Documentation
- Code review
-Test and application recipe - Collection of need in close collaboration with the profession
- Technical environment:
- Agile SCRUM Methodology
- C #
- Git, JIRA
- AzureDevOps
-Azure machine learning studio - Agile SCRUM Methodology
Expert ChatBot
BNP Personal Finance
June 2018
to August 2018
- Context:
BNP Paribas Personal Finance, a subsidiary of the BNB Paribas group, offers a full range of personal loans available in stores, in car dealerships or directly from customers via its customer relations centers and on the internet.
As part of the credit simulation, I joined an Agile team: Digital Lab, in charge of creating a credit simulation chatbot.
Team:
1 scrum master, 4 in, 3 developers
Project organisation:
Agile (Sprint of a week) - Contribution:
- Establishment and development of the Bot architecture.
- Collection of need in close collaboration with the trade (PO)
- Implementation of unit tests and integration test
- Chatbot functional tests
- Establishment and development of the Bot architecture.
- Technical environment:
- Agile SCRUM Methodology
- Crafts: Unit Testing, TDD
- language: Nodejs
- Agile SCRUM Methodology
Expert Artificial Intelligence / Machine Learning
Natixis
October 2016
to May 2018
- Context:
As part of the digital transformation of BPCE, I joined an Agile team, responsible for creating an application that predicts the re-socialization of Front Office reconciliations. The team includes collaborators who have never practiced Agile methods,
So I upgraded the team members to Agile methods to improve their autonomy and efficiency.
Development of a tool based on Machine Learning and artificial intelligence frameworks to automate the creation of reconciliations.
Maintain an active watch on the theoretical and technological topics of data science.
Create a ChatBot for support automation of a Front Office reconciliation application.
Team:
1 Scrum Master, 7 Developers, 1 PO Proxy (which links the clients to the team), 3 MOA
Project Organization:
Agile (2 weeks sprints).
Part of the team is in Portugal (3 developers), the rest of the team in Paris. - Contribution:
Set up a prediction tool for creating bank reconciliations via the strategies of artificial intelligence (CSP) and machine learning (classification algorithms).
Functional needs analysis in close collaboration with the business.
Implementation of the ChatBot architecture.
Preliminary study of the different existing frameworks for the creation of a chatbot.
Implementation of different technologies to use.
Chatbot functional tests - Mission:
Knowledge in market finance (options market, stock market and bonds)
Design, analyze and maintain databases,
Create, calibrate quantitative prediction models using Machine Learning and Artificial Intelligence (CSP) techniques
Synthesizes and communicates the results of mass data analyzes to extract a decision-making knowledge
Enhance existing algorithms and develop new algorithms for creating automatic reconciliation of Front Office deals.
Creation of ChatBot architecture.
ChatBot integration with a Front Office reconciliation web application.
data trapping and visualization - Results:
Development of a ChatBot for functional and technical support automation
Creation of a predictive tool for creating bank reconciliations - Technical environment:
Computer programming: Python (scikit-learn, Numpy, SciPy, Pandas, JSON, CSV, XML, CSP, mysql.connector, pytest, unittest)
Algorithms and database management: SQL
Agile SCRUM Methodology
Crafts: Unit Testing, TDD
