Internship - Recommendation Engines for Marketing
il y a 2 semaines
You will be integrated to the AI & Data Analytics team which main scope is to deliver transformational insights with value impact on all related businesses of Saint-Gobain.
The purpose of this internship, in collaboration with the team, is to benchmark classic approaches in comparison to recent approaches, choosing the right metrics of validation, with the focus on recent approaches of collaborative-filtering such as: autoencoder, recurrent neural network and transformers.
The results of this internship will be directly used operationally in the field.
***Core responsibilities**
- Read, present, and implement research papers in the relevant areas (ML, DL, Recommendation engines)
- Select promising algorithms for benchmarking depending on the business case.
- Develop clean and generalized code which can be adapted in the general tool-box
- Work closely with engineering teams to push innovative learning models to production
- Contribute to Saint-Gobain Recommender product
**Technologies we use**
- Python, Open-source libraries (sklearn, implicit, scipy)
- Azure Machine Learning
REQUIRED QUALIFICATIONS
- Being friendly and open to collaboration
- Good programming skills and familiar with Python/Git and open source tools
- Results oriented; with a motivation to have an impact on the field
- MS or equivalent in Machine Learning, Computer Science, Statistics or related fields
- Experience in recommendation is plus
- Student in 4th or 5th year of engineering school or university, specialized in Machine Learning, Computer Science, Statistics or related fields
ADDITIONAL REQUIREMENTS
**CONTEXT**
Recommending similar or complementary products for the purpose of cross-sale or up-sale in e-commerce or CRM platforms is an important and challenging task.
Currently our team is working to provide general recommendation tools that can be adapted and used for different businesses of Saint-Gobain.
Recommendation systems are traditionally divided into several groups of:
- Content-based: recommending items to users based on items similarities
- Collaborative-filtering: recommending items to users based on other users behavior
- Knowledge-based: recommending items to users based on basic knowledge of users, items and relationship between items
- Hybrid-based: recommending items based on combination of more than filtering approach.
WHO ARE WE ?
Who we are, as Saint-Gobain:
At Saint-Gobain, our purpose is to 'Make the world a better home.' We, as Saint-Gobain, are a top global employer, a historical company in France and Europe, and a worldwide purpose-driven leader in the building materials industry and lightweight constructions. We have a long-term ambition to make our planet a better home. We are involved in building a more sustainable and inclusive world to help protect the environment and leave the world to future generations better than we found it.
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