Product Data Scientist

Who we are
At Criteo, we are building the advertising platform of choice for the open Internet, an ecosystem that favors neutrality, transparency and inclusiveness. With more than 1.4 billion active shoppers and $600 billion in annual commerce sales, we deliver performance at scale. Founded in a Paris start-up incubator, Criteo now carries out our entrepreneurial spirit across 30+ global offices. Do you want to have an impact on more than half of the world’s internet users? Join us and be part of something big.

We are looking for a highly motivated Data Scientist to join our growing Product Analytics & Data Science team. The team sits in Product, between the business and technology groups; we have a strong quantitative culture, and our analysts and data scientists are involved in setting the agenda and driving decisions across the organization.

What will you be doing?
– Developing full stack, end-to-end data products with a focus on: Anomaly and Fraud detection, Forecasting, Others

– Working in collaboration with Sales and Operations, Product, Software Engineering and Research teams, giving you a unique 360 degrees view of Criteo’s business.

– We continuously test new models, try new approaches and launch new projects, so you will be taking advantage of your creativity in designing and implementing entirely new machine-learning models and strategies, but with enough pragmatism to not waste time on things that will bring little value to Criteo.

What do we expect?
– Master’s or Ph. D in a quantitative field (Statistics, Mathematics, Computer Science, Economics, etc.)
– At least 2 years of prior work experience sourcing, cleaning, manipulating and analyzing large volumes of data and building ML models using Python or R.
– You must have a genuine interest for real-world business problems, be proactive, creative and eager to learn.
– Strong intellectual curiosity and ability to structure and solve difficult problems with minimal supervision.
– English fluency
– Prior experience in time-series modeling and/or anomaly detection ML is a plus.

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