The role

Build the prioritisation engine:

business rules, objective function and expected-value scoring per company/product.
Architect data pipelines on AWS

(Glue, Step Functions, Lambda, S3, Athena, Bedrock) that replace static lists and manual CRM/Excel crosses with automated opportunity identification.
Lead a cross-functional team of Data Engineers and Data Scientists , mentoring them on architecture, data quality and the integration of scoring/ML models into production.
Build full traceability into the system , target perimeter vs. covered perimeter.
Design the criteria-governance and market-intelligence layers as a reusable standard across business units, banks and countries.
Collaborate closely with Product and Business teams

to turn real banking pain points into a robust, sellable product.
What we're looking for

Strong

quantitative and analytical mindset , comfortable translating business criteria into scoring and prioritisation logic.
At least

5 years of experience in data-intensive roles

(Data Engineering, Analytics or Data Science), with at least





2 years leading or coordinating technical teams.
Solid experience designing and operating

AWS cloud architectures for data workloads

(S3, Glue, Lambda, Step Functions, Athena, RDS).
Expert knowledge of

Python for data engineering and analytical applications; SQL proficiency for complex querying and data modelling.
Demonstrated experience building scoring, prioritisation or recommendation systems that combine business rules with data-driven models (next-best-action, lead scoring, opportunity prioritisation, or equivalent).
Fluency in Spanish and English.
This gives extra points

Background in

Mathematics, Operations Research, Statistics, or other quantitative disciplines.
Hands‑on experience with constrained/combinatorial optimisation, multi‑armed bandits, or reinforcement learning applied to business decisioning.
Experience with Big Data frameworks ( PySpark, Glue ) and distributed data processing.
Re

📌 Data Lead (Madrid)
🏢 mscope
📍 Madrid