Data Scientist
Summary
Data Scientist at FERO (Madrid, hybrid) who independently owns ML models end-to-end — defining problems, running experiments, deploying to production and measuring impact. First priorities: improving the payment-method usage prediction model and scaling an ensemble recommendation model, using Python, pandas/polars, scikit-learn and AWS.
Madrid (Hybrid)
Full-time | Permanent
About FERO
FERO is reinventing checkout for eCommerce.
We build high-performance, adaptive checkout experiences that help merchants increase conversion and revenue.
Backed by top-tier VCs, we’re building from Madrid with a strong focus on performance, data, and real impact.
About the role
We’re looking for a Data Scientist to help turn FERO’s data into smarter product decisions and measurable improvements for our customers.
You’ll independently take modelling opportunities from problem definition through experimentation, production deployment and impact measurement. Your first priorities will include improving our payment-method usage prediction model and scaling our ensemble recommendation model.
Our data function and platform are still evolving, so you’ll need to be comfortable managing your own workload, collaborating closely with Product, Engineering and Data colleagues, and working in an environment where not every process or data foundation is fully established yet.
What you’ll do
- Improve our multiclass payment-method usage prediction model.
- Help scale and develop our ensemble recommendation model.
- Turn business and product questions into testable hypotheses, experiments and modelling opportunities.
- Develop and validate classification and recommendation models.
- Design and analyse A/B tests, holdouts and other experiments to measure business impact.
- Evaluate models through offline validation, backtesting and production performance.
- Deploy models into production and monitor how they perform.
- Work closely with Product, Engineering and other stakeholders to translate insights into practical decisions.
- Help replace manual rules and heuristics with learned models where this creates measurable value.
🧠 What you’ll bring
- 3–5 years of professional data science experience, including models that reached production.
- Strong Python skills and experience with tools such as pandas or polars and scikit-learn or equivalent.
- Strong statistical and experimentation fundamentals, including hypothesis testing, A/B testing, and holdout design.
- Practical experience with pattern or association mining, language models, or both.
- Experience validating classification models through offline evaluation and backtesting.
- The ability to connect your work to measurable business outcomes, not only build a model but demonstrate the impact it created.
- Strong communication skills and the ability to translate between business questions, technical approaches, and actionable results.
- Comfort working in an environment where the data platform and foundations are still being built.
➕ Good to have
- Experience deploying models on AWS using tools such as SageMaker, ECS, or EKS.
- Experience replacing heuristics with learned models in conversion, growth, recommendation, fraud or risk contexts.
- Experience working with payments, fintech, or other transaction-based data.
What we offer
- Permanent contract.
- Private health insurance (partially covered).
- Hybrid work (3 days office / 2 remote).
- Office in a central location in Madrid.
- Real growth opportunities.
- A high-impact, collaborative environment.
🧭 Hiring process
- Screening call.
- Technical interview
- Final conversation