Data Science Data Science Data Modeler

its architecture, existing machine learning systems, and data pipelines. Together with other Data Scientists and engineers, you'll learn how search, recommendations, and shopper engagement models currently operate while getting familiar with the team's ongoing initiatives. You'll deliver your first meaningful improvements to the Shopper experience, contributing to search quality or recommendation performance. You'll help define and evaluate recommendation models, establish key success metrics such as CTR, conversion, nDCG, MRR, and zero-result rate, and participate in experimentation through A/B testing to validate model impact. Our machine learning platform runs on AWS and is built on top of a modern data ecosystem that supports experimentation, production deployment, and continuous model improvement. We use Python as our primary development language, with ML workflows powered by SageMaker and orchestrated through Airflow. Our data platform relies on Redshift and S3 for storage and

processing, while Kubernetes provides the infrastructure for scalable model serving and supporting services. Models are continuously evaluated through offline metrics and online A/B testing to ensure measurable product impact. 5+ years of experience as a Data Scientist / ML Engineer , with a strong track record of building and deploying models in production. ~ Hands-on experience with recommendation systems - collaborative filtering, content-based, or hybrid approaches, including cold-start and personalization. ~ Experience working on product-facing ML (search, ranking, personalization, or similar), where models directly shape user experience. ~ Strong Python and SQL ; experience with the full ML lifecycle (data sourcing, feature engineering, training, evaluation, deployment, monitoring). ~ BM25, embeddings/KNN, OpenSearch/Elasticsearch) is a strong plus. ~ Solid grounding in clean coding, testing, and agile ways of wo

seQura provides innovative, flexible and easy-to-use payment technologies that help merchants acquire, convert and retain more customers. We make a difference in sales performance by tailoring our solutions to different sectors, to address their unique pain points and deliver superior results in Retail, Education, Eyewear, Repairs and Travel. We also empower smart shopping to consumers who seek more value, convenience, and flexibility in their shopping, with new payment experiences that allow them to save, access interest-free credit, or pay in small, comfortable instalments of up to 24 months. Born in Barcelona, seQura is a privately-owned fintech, currently expanding throughout southern Europe and Latin America, growing above 50% CAGR and approaching 100 million in Annual Recurring Revenue. We are looking for a Senior Data Scientist to help design, build, and evolve the intelligence behind seQura's Shopper App - a shopping app where users manage the payments they've made with seQura and discover and shop across merchants with rewards. This role focuses on building production-grade machine learning systems that bring intelligence into real user flows. You will work on smart search, recommendations, and agent capabilities - models that understand context, reason over shopper needs, and help users discover and shop in ways that are relevant, personalized, and safe. You will collaborate closely with Product, Frontend, Data, and AI teams, playing a key role in shaping both the technical approach and the shopper experience. Owning the full model lifecycle: sourcing and modeling data, feature engineering, training, evaluation, and deployment, with strong attention to detail and product ownership. Working closely with Product, Frontend, Data, and AI teams as a strong team player and communicator, driving alignment across disciplines. About the Data team Through machine learning, search, and AI, the team turns data into intelligent product capabilities that improve shopper engagement, increase conversion, and drive long-term customer value. Recommendation systems, including collaborative filtering, content-based, and hybrid approaches The full machine learning lifecycle, from data exploration and feature engineering to model deployment and production monitoring Experimentation frameworks and ML performance measurement The Data Science & AI team works as an embedded product team alongside Software Engineering, Product, and Design. Data Scientists partner closely with Product Engineers to bring machine learning models into production, while collaborating with Data Platform to ensure scalable infrastructure and reliable ML workflows. End-to-end ownership across the entire machine learning lifecycle. Rapid experimentation through A/B testing, offline evaluation, and continuous iteration. Continuous improvement driven by data, experimentation, and user behavior. You'll onboard with the Data Science team and gain a deep understanding of the Shopper App ecosystem