Applied Scientist – Search, Ranking & Recommendations

Our client, a prominent player in the digital retail sector, is looking for an Applied Scientist to enhance their discovery and recommendation engines. The role involves optimizing learning-to-rank models and supporting strategic technical evaluations within a dynamic engineering environment.

• Build and optimize end-to-end machine learning pipelines for semantic query understanding, product recommendation, ranking, and personalization.
• Collaborate with product and software engineering teams to deploy scalable solutions into production.
• Develop custom feature store architectures and automated retraining pipelines to ensure model consistency and combat drift.
• Maintain robust monitoring and scalability protocols for production-grade machine learning systems.
• Lead technical decision-making regarding algorithms and data transformations for research-driven enhancements.
• Provide technical insights and engineering rigor during strategic evaluations of search and discovery capabilities.

• You have a proven track record of delivering machine learning projects with measurable production impact and familiarity with MLOps.
• You bring experience operating in a hybrid capacity between Data Science and Machine Learning Engineering.
• You possess expertise in Python, SQL, and Statistics alongside Machine Learning and Deep Learning concepts.
• You have experience with deep learning frameworks such as PyTorch or TensorFlow.
• You bring knowledge of deployment and orchestration tools including AWS SageMaker, Airflow, and FastAPI.
• You have experience with cloud infrastructure and containerization using AWS, Docker, and Kubernetes.
• You possess advanced skills in data storage and management using Snowflake, Redis, OpenSearch, and dbt.
• You're experienced in implementing and analyzing A/B testing.
• You bring strong communication skills to navigate complex architectural discussions and align with diverse stakeholders.
• You are fluent in English.

Nice to Haves

• Experience with fashion discovery or e-commerce personalization.
• Familiarity with strategic "buy vs. build" technical evaluations.

• Start date: ASAP
• Duration: 6 months
• Work regime: Full-time
• Location: Berlin or Paris
• Working model: Hybrid
• Contract: open to both permanent employees and freelancers