About seQura
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.
Over 5000 businesses, almost 2 million shoppers, and almost 400 employees continue to rate us as one of the most loved and trusted fintechs out there, with an NPS of 87%, a Trustpilot rating of 4.7/5, and a Glassdoor rating of 4.7/5.
About the role
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.
What challenges you'll be solving
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Designing, building, and shipping the recommendation systems that power the Shopper App, helping users discover relevant merchants, products, and offers across the full shopping lifecycle, while writing simple, clean, and efficient code.
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Improving smart search — relevance, ranking, intent detection, and query understanding — so shoppers quickly find what they're looking for.
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Turning ambiguous shopper needs and product ideas into reliable, scalable ML systems through a pragmatic, analytical, and accountable approach — favoring simplicity and iteration over premature complexity.
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Owning the full model lifecycle: sourcing and modeling data, feature engineering, training, evaluation, and deployment, with strong attention to detail and product ownership.
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Defining and tracking the metrics that matter (CTR, conversion, nDCG, MRR, zero-result rate) and running experiments to measure real impact on shoppers.
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Working closely with Product, Frontend, Data, and AI teams as a strong team player and communicator, driving alignment across disciplines.
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Driving change and efficiency by collaborating with partners and stakeholders from different disciplines, bringing motivation to learn, grow, and challenge the status quo.
About the Data team
Team mission
To build the intelligence behind seQura's Shopper App, creating personalized shopping experiences that help millions of users discover the right merchants, products, and offers at the right moment.
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.
What we own
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Recommendation systems, including collaborative filtering, content-based, and hybrid approaches
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Smart search capabilities, including relevance, ranking, intent detection, and query understanding
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Shopper recurrency and Lifetime Value (LTV) prediction models
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AI-powered product capabilities and intelligent agents
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The full machine learning lifecycle, from data exploration and feature engineering to model deployment and production monitoring
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Experimentation frameworks and ML performance measurement
Team Structure
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.
How we work
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Product-first mindset: every model is built to solve a real user problem and create measurable business impact.
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End-to-end ownership across the entire machine learning lifecycle.
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Rapid experimentation through A/B testing, offline evaluation, and continuous iteration.
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Close collaboration with Product, Engineering, and Design to integrate AI into the Shopper experience.
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Continuous improvement driven by data, experimentation, and user behavior.
What to expect in the next 90 days
Month 1
You'll onboard with the Data Science team and gain a deep understanding of the Shopper App ecosystem, 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.
Month 2
You'll take ownership of existing workstreams and begin contributing improvements to our smart search capabilities, including relevance and ranking. You'll also collaborate closely with Product and Engineering to understand the current recommendation engine architecture and identify opportunities for future improvements.
Month 3
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.
Tech stack & environment ️
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.
The team works extensively with modern recommendation systems, information retrieval techniques, LLM-powered applications, and experimentation frameworks. Models are continuously evaluated through offline metrics and online A/B testing to ensure measurable product impact.
What you’ll need
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5+ years of experience as a Data Scientist / ML Engineer, with a strong track record of building and deploying models in production.
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Hands-on experience with recommendation systems — collaborative filtering, content-based, or hybrid approaches, including cold-start and personalization.
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Experience working on product-facing ML (search, ranking, personalization, or similar), where models directly shape user experience.
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Strong Python and SQL; experience with the full ML lifecycle (data sourcing, feature engineering, training, evaluation, deployment, monitoring).
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Familiarity with search and information retrieval (e.g. BM25, embeddings/KNN, OpenSearch/Elasticsearch) is a strong plus.
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Solid grounding in clean coding, testing, and agile ways of working.
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Initiative and ownership — you spot what needs doing and drive it forward, not afraid to use your voice, share your opinion, and challenge assumptions when something can be done better.
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Adaptability — you're comfortable with ambiguity and shifting priorities, and you learn fast in a fast-moving environment.
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English proficiency.
What we offer
We have a strong and sustainable foundation, where we provide a secure and reliable workplace. You have the freedom and trust to make the best contribution possible.
One of our most valued strengths by our employees is our fellowship and supportive culture, which fosters a sense of belonging by working closely with our values. With us, you will have challenging projects to work on and push your skills and knowledge.
In addition, we are very proud of the unique office we have, which offers a comfortable and inspiring environment to work in with everything you need.
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23 vacation days + 2 days of free disposal per year.
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Flexible compensation plan for transportation, restaurants, and kindergarten with Cobee.
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Health insurance discounts with Sanitas and DKV.
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Flexible working hours.
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A personal budget for professional development.
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Office workshops and meet-ups to encourage community participation and career growth.
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Hybrid or remote work (up to 2 hours difference from GMT+2).
Moreover, we have a Wellness Program that embraces a holistic approach by covering 6 areas (occupational, physical, financial, emotional, social, and environmental consciousness). Each area will include a variety of activities, and you'll be able to choose from 34 different activities that best meet your needs to configure a plan that best works for you.
Which are the next steps?
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Interview with People Team
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Manager interview
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Technical take-home assessment (async)
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Live Peer review of the assessment
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Meet the team
We kindly ask that you submit your CV in English, as it is the official language of our community.
We promote equal opportunity to all, regardless of age, color, gender identity, medical condition, physical or mental disability, race, religion, sexual orientation, or any other characteristic. We have an inclusive environment, and respect is above all.
Do you want to be part of the change? Join us!
