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Qikai Alex G.

data scientist / machine learning engineer

Paris, France

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Github : qikaigu qikaigu
  • 24 Repos
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Kaggle : Qikai Qikai
  • Expert Tier
  • 4026 Rang
  • 2 Bronze
  • 0 Argent
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Stack Overflow

Stack Overflow : Qikai Qikai
  • 26 Réputation
  • 6 Bronze
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Rejoignez la communauté Malt pour découvrir le profil complet de Qikai Alex G.
Rejoignez la communauté Malt pour découvrir le profil complet de Qikai Alex G.

Compétences (18)

Qikai Alex en quelques mots

6+ years of experience in building end-to-end machine learning applications, solid fundamentals of data structures and algorithms. Cross-functional: be able to exchange with data analysts, data engineers, backend engineers and product teams.

I can help you build a scalable end-to-end machine learning applications on AWS. Leave me a message if you are looking for long term collaboration or want to rapidly prototype an MVP during a weekend.


novembre 2018 - Aujourd'hui | Paris, France

Cinéma & audiovisuel

M6 Digital Distribution- M6

Data Scientist

Automated Machine Learning Platform
• Build a general-purpose automated Machine Learning platform based on Apache Spark framework.
• Build a cross-platform (desktop, mobile and TV) lookalike audience extension system on top of the general-purpose ML platform, which extends the audience reach up to 6 times larger than the base audience.
• Build a video recommender system on top of the general-purpose ML platform serving millions of users.
mars 2014 - septembre 2017 | Paris, France


A little Market - Etsy France

Data Scientist

Search Ranking
• Created the first Machine Learning powered application for millions of unique items on
• Designed and developed a user behavioral data collecting system with Node.js and Elasticsearch.
• Developed a unique item purchase predictive model in Python(scikit-learn) and an Algolia reranking process.
• Deployed the predictive model in production as API using Flask.
• Designed and monitored Key Performance Indicators in Google Analytics and internal tools.
• Increased revenue by 15% (A/B test).

Recommender System
• Implemented a content-based recommender system with Spark Scala for millions of unique items.
• Parallelized batch jobs by using MapReduce programming model.
• Reduced job duration by optimizing algorithm's time complexity.

Autocomplete Suggestions
• Built a pipeline in Python for extraction and spelling correction of e-commerce expressions in French.
• Built a parallelization tool to reduce processing time with Python multiprocessing.

Recommandations externes




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