Senhaji Rhazi Hamza

python + vue js + (docker/kubernetes)

Paris, France

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Rejoignez la communauté Malt pour découvrir le profil complet de Senhaji Rhazi Hamza
Rejoignez la communauté Malt pour découvrir le profil complet de Senhaji Rhazi Hamza

Compétences (9)

  • Containerization
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Senhaji Rhazi en quelques mots

+ J'ai une formation de data-scientist à la base, forte compostante mathématique EPITA (PROMO SCIA 2018)
+ J'ai effectué une mission freelance data-science (4mois) pour une startup qui faisait de la prédiction
d'évolution de reserves de puits petroliers
+ J'ai travaillé pendant 1an et 4 mois en tant que data engineer à Artefact, ou :
++ j'ai développé du produit sur cloud (GCP, AWS) backend-front (python, flask, sqlAlchemy, vue js)
++ j'ai travaillé sur des pipeline sur de la grosse donnée en utilisant Kubernetes + Apache airflow + BigQuery + python
+ J'ai effectué chez HaroldWaste un travail de construction de micro-service back-end (python + sqlAlchemy) + aidé à developpé quelques features sur leur produit, à la fois sur le web et mobile, en utilisant la stack (react, react native, graphql, node js)

Aujourdh'ui je suis ouvert sur des missions à composante infra sur GCP + kubernetes et développement de produit full-stack avec les technos python + vue js

Expériences

mai 2020 - Aujourd'hui | Paris, France

Logistique & Supply Chain

HaroldWaste

Full-stack devops

At HaroldWaste i worked on product's features development, I have also built independent micro-service as
a module helper for their main product
février 2019 - avril 2020 | Paris, France

E-commerce

Artefact- ARTEFACT

Data engineer

I worked at Artefact as a data engineer, my work mainly consisted in building and designing softwares and architectures that might ship data-scientist's models (shipping them in a product), or handle big amount of data (ETL for example)

Some projects worked on at Artefact :

Chatbots with international beer company:

- Description :

I worked within a team on developing two messenger chatbots for this company,
the first chatbot was build as loyalty consumer program, where a consumer take a
picture of his consumption (either a bill of purchase or a special glass), this picture is
analyzed by machine learning models, then once the picture is recognized, the consumer
receive some credits points which gave him some privileges




The second messenger chatbot was treating automatically most of the customer complains

- Technologies used :

- Python (sqlAlchemy + flask)
- Dialogflow
- Google cloud platform
- PostgresQL



Media dashboard for an international pharmaceutic company

- Description :

This company make advertisements for some of their drugs using different
canal (facebook, instagram, youtube, google ads, dv360, google analytics)

In order to help this company to improve the efficiency of their marketing
campaign we built for them a pipeline data architecture + dashboard,
that allows them to better track the performance of their advertisements campaigns

- Technologies used :

- Python
- Amazon web service (S3)
- Docker
- Kubernetes (jobs scheduling)
mars 2018 - septembre 2018 | Paris, France

Edition de logiciels

reciTAL

Intern R&D NLP Engineer at reciTAL

My work at reciTAL consisted on doing research on the NLP (natural language processing) task, more specifically for QA systems (Question answering systems) and develop a commercial product out of it. In other words developing an intelligent search engine, where people can query their unstructured data in human language.

QA systems task is a sub-field of NLP which is a sub-field of AI, where the challenge is to query over unstructured or semi structured data like a Wikipedia articles for example, and have a system that can point the answer on the article, Stanford university have launched a competition over this task released a famous dataset (SQUAD dataset ) and people all over the world are competing

During my internship, I had to do a research part in order to improve the models and adapt them for French as well as English and develop a commercial product using those models, with all what it implies. At that time our work at reciTAL lead us to occupy the 3rd place in one of the task (predecting the existence of answer) on the leader-board.

3 recommandations externes

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