Senior Applied Scientist (M/F)

Context of the AI Product Team

The AI Product team builds end-to-end products, leveraging cutting-edge Machine Learning and AI technologies to optimize the customer journey and internal efficiency. Our main scope includes capabilities related to liquidity (recommendation systems, search) and trust & security (fraud models, duplicate detection, reputation score). We are notably in the launch phase of a major transition from a rule-based marketplace to an AI-enriched platform, with an initial focus on Recommendation and Search systems.

Role

As an Applied Scientist, you are the driving force behind the research and development phase of AI models, ensuring alignment between technological advancements and the company's strategic objectives.

You will work under the management of the Applied Science Team Manager, who will coordinate priorities and delivery.

Key Responsibilities

1. AI/ML Model Research and Development (Research Phase):

• Lead the research, evaluation, and experimentation of AI and Machine Learning models (e.g., Deep Learning for recommendation, Gen-AI, NLP/Vision for search) to solve complex business problems.

• Select appropriate data sources and determine key metrics to track and optimize (e.g., conversion rate, hit rate, precision/recall).

• Develop prototypes and prove the feasibility and the expected impact of concepts before industrialization.

• Be the technical reference for the state-of-the-art in AI/ML applied to the marketplace.

2. Collaboration and Industrialization (Deployment Phase):

• Work closely with Machine Learning Engineers (MLE) to ensure that developed models are robust, scalable, and ready for production deployment.

• Actively contribute to the deployment and integration phase of models within the technical architecture (Snowflake, AWS).

3. Monitoring and Continuous Improvement:

• Participate in monitoring models in production, focusing on detecting drift and performance degradation.

• Design and propose iterations (A/B testing) to continuously optimize the performance of AI products.

4. Expertise and Education:

• Ensure constant technological watch on advancements in AI/ML.

• Support and guide more junior team members.

• Document and share knowledge and best practices within the team and with other stakeholders (Product Managers, Business).

Required Profile
Technical Skills & Experience:

• Deep expertise in Statistics, Machine Learning and Deep Learning (e.g., recommendation models, NLP, Computer Vision, Gen-AI). A previous experience with a marketplace business will be valued.

• Proficiency in common programming languages for Data Science (Python, SQL).

• Familiarity with cloud computing environments (AWS) and data platforms (Snowflake).

• Experience in conducting research work:

  • Forming research hypothesis

  • Implementing experimentation methodologies (Offline analysis, A/B testing) to test them

  • Statistical analysis of results.

Team Work and Communication:

• Loves to actively collaborate with other Applied Scientists in a kind and constructive environment during research phase, reviews, discussions and knowledge sharing.

• Excellent communication skills to closely interact with technical and non technical stakeholders (MLE, MLOps, Data Engineers, Product).

Result and Rigor Oriented Mindset:

• Pragmatism while balancing and prioritizing research hypotheses to iterate and continuously improve the models.

• Focus on simplicity and problem solving solutions that have business impact.

• Strives for excellence.

Autonomy & Ownership:

• Proactivity, autonomy, and ability to take leadership of the research phase.

• Takes pride in building high quality data products while being open to feedback and continuous learning.

What We Offer

• The opportunity to work on high-impact AI projects, at the heart of the growth strategy of a digital leader.

• A modern technological environment and a multidisciplinary team (MLE, MLOps, Applied Scientists) working autonomously and end-to-end.

• A key role in building the next generation of Recommender System and Search products.

Deep expertise in Statistics, Machine Learning and Deep Learning (e.g., recommendation models, NLP, Computer Vision, Gen-AI), with previous experience in a marketplace business being valued.

Proficiency in common programming languages for Data Science (Python, SQL), familiarity with cloud computing environments (AWS) and data platforms (Snowflake).

Experience in conducting research work including forming research hypotheses, implementing experimentation methodologies (Offline analysis, A/B testing), and statistical analysis of results.

Strong collaboration skills and excellent communication abilities to interact with technical and non-technical stakeholders.

Result and rigor oriented mindset with pragmatism, focus on simplicity and business impact solutions, and striving for excellence.

Proactive with autonomy and ability to take leadership of the research phase, taking pride in building high quality data products while being open to feedback and continuous learning.