Machine learning engineer in recommendation systems

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• Develop real-time product and bundle recommendation engines, including dynamic slot placement optimized for conversion and margin.
• Build targeted promotion algorithms: personalized coupon and points issuance, behavioral triggers and dynamic bundling.
• Scale the multi-market customer segmentation engine (behavioral, demographic, psychographic and lookalike audiences, wallet push).
• Extend personalization models across web, app and in-store employee touchpoints.
• Build and operate ML pipelines, from feature preparation in BigQuery and Snowflake to deployment and monitoring.
• Design and evaluate A/B tests to prove the business impact of each model.

• Hands-on experience building ranking or recommender systems that run in production.
• Strong Python and SQL skills; experience with TensorFlow or PyTorch.
• Solid grounding in model evaluation and validation, both offline metrics and online results.
• Experience designing and analyzing A/B tests and experiments.
• MLOps experience: ML pipelines, model deployment, performance monitoring and CI/CD.
• Experience with cloud data warehouses, ideally BigQuery and/or Snowflake, and pipeline orchestration with Airflow.
• Experience with real-time or low-latency model serving.
• Experience with promotion, pricing or uplift modeling (e.g. coupon targeting, bundling).
• Customer segmentation and lookalike modeling across several markets.
• Infrastructure as code with Terraform.
• Experience on GCP ML tooling such as Vertex AI.
• Background in retail or e-commerce personalization.
• Fluent English for working in an international team.

• We nurture genuine connections, encourage an active lifestyle, and support the development of a healthy mindset. Our benefits programme is inspired by Maslow’s hierarchy of needs and is designed to prioritise your overall wellbeing.
• To live a wholesome experience inside and outside our offices, we need to take care of our overall wellbeing, from the physical aspect to creating a healthy work-life fusion and looking at learning as a lifelong journey and a shared responsibility between our people and our organisation.
• Our wellbeing program includes medical insurance, In-house fitness trainer available remotely and 1 on 1 remote sessions with our Sports and Health Coach focused on nutrition counselling and tailored training plans.
• Start with at least 21 vacation days (based on seniority) and earn 1 extra day for each completed year of collaboration — up to 29 days — depending on your tenure in the company and/or industry. Enjoy the flexibility to work from both home and the office.
• We focus on long-term learning programmes that develop both technical and non-technical skills incrementally. Together with your Career Coach you will discuss what are learning resources and platforms you can access in order to develop your skills.