Data Scientist

About the role

You will join a Data Science team that is distributed across Paris, Lyon, Barcelona, and Brussels, operating at the center of a major European e-commerce flash sale platform. Your primary mandate will be the design of experiments that govern how offers are presented to users during highly constrained flash sale windows. In this role, you will own the conversion probability models that must react to rapidly shifting brand catalog availability in real time. You will translate complex behavioral signals from millions of users into coherent ranking logic that respects the time-sensitive nature of inventory. Daily choices you make will directly align user intent with the limited duration of promotional offers across fashion, home goods, wine, travel, and beauty. The position requires you to construct intake pipelines that convert raw click events into structured training signals for ranking models. You will be responsible for establishing monitoring routines that detect feature drift and alert stakeholders when behavioral patterns diverge from historical expectations.

Key facts

What you'll do

• Craft intake pipelines that convert raw click events into structured training signals for ranking models.
• Construct models designed to forecast the probability of conversion while accounting for fluctuations in brand catalog availability.
• Analyze validation results to verify that patterns learned during training remain robust when market conditions shift unexpectedly.
• Establish monitoring routines to track feature drift and notify stakeholders when behavioral patterns diverge from expectations.
• Collaborate closely with ML and Data Engineers to transition experimental algorithms into production serving layers capable of handling live requests at scale.
• Provide guidance to partners on the interpretation of model outputs to ensure that recommendations support commercial objectives.
• Maintain documentation detailing data definitions, constraints, and underlying assumptions for future analysts and stakeholders.
• Execute experiments that test alternative ranking strategies and measure their impact on downstream engagement metrics.
• Support cross-functional initiatives involving the travel, wine, and home categories to ensure model applicability across diverse verticals.
• Advocate for practices that ensure recommendations are transparent, auditable, and respectful of user privacy.
• Work on models that deliver personalized sales recommendations to millions of users in real time, with scope expanding to hundreds of thousands of products.
• Ensure that the algorithms align user intent with inventory that is available for a limited duration during flash sale windows.
• Evaluate the tradeoffs between model complexity and latency when communicating with non-technical stakeholders.
• Implement solutions that convert complex behavioral signals into coherent ranking logic suitable for time-sensitive environments.
• Contribute to the continuous enhancement of existing recommender systems that are currently delivering personalized sales recommendations.

Requirements

• Possess a minimum of three years of experience building models for user behavior in production settings.
• Demonstrate proficiency in Python, along with the ability to write queries that scan large tables of events efficiently.
• Show a solid understanding of recommender systems concepts, including embeddings, similarity metrics, and ranking losses.
• Articulate the tradeoffs between model complexity and latency to non-technical stakeholders clearly and effectively.
• Hold the ability to work within a distributed Data Science team across Paris, Lyon, Barcelona, and Brussels.
• Understand the importance of converting raw click events into structured training signals for machine learning models.
• Comprehend the necessity of monitoring feature drift and maintaining robust validation patterns in dynamic market conditions.
• Commit to advocating for transparency, auditability, and user privacy in recommendation practices.

Nice to have

Experience with collaborative filtering or content-based approaches within e-commerce contexts is considered a plus.

Practical notes

• Hours: Standard working hours apply.
• Travel: Not applicable.
• Visa: Not applicable.
• Deadlines: Apply on the official posting to confirm current deadlines and procedures.

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