Lead Data Scientist - Credit Risk (Collections)

Excited to grow your career?

BBVA is a global company with more than 160 years of history that operates in more than 25 countries where we serve more than 80 million customers. We are more than 121,000 professionals working in multidisciplinary teams with profiles as diverse as financiers, legal experts, data scientists, developers, engineers and designers.

Learn more about the area:

BBVA AI Factory operates as a global hub within the Data area of BBVA, with development centers in Spain, Mexico, and Turkey.

Our mission is to build complete, end-to-end data products that solve BBVA's business needs by working closely with business units to transform strategic priorities into actionable, data-driven solutions. Some of our recent projects include:

Mercury Library, an in-house AI framework now available to the entire data community, aimed at boosting collaboration and accelerating AI solution development.

A machine learning pipeline designed to enhance early debt recovery by predicting default risk and optimizing collection strategies

Applying daily life embeddings to drive deeper personalization in customer interactions and improve service recommendations.

Utilizing conformal prediction to provide reliable uncertainty estimates and enhance the confidence in AI model predictions

Building algorithmic explainability frameworks to ensure transparency and foster trust in our AI systems.

At BBVA AI Factory, innovation isn’t just a goal-it’s a continuous journey.

Why You'll Love Working Here

Be part of a team that helps create an easier, more personalized banking experience offering better service to our customers.

Work on incorporating state-of-the-art AI to improve key bank processes like fraud detection, risk management, and debt management.

Join us in developing a new customer relationship model supported by AI, benefiting both end customers and managers.

Collaborate with diverse teams composed of professionals from different disciplines, including data science, machine learning engineering, solution architecture, developers, analysts, and product experts.

Embrace our obsessions: pursuing innovation, developing reusable components, and reaching the customer as quickly as possible.

About the job:

*Vacante publicada hasta el 17 de diciembre del 2025.

Key job responsibilities:

Strategic & Analytical Leadership

Act as the analytical reference for the Collections program, ensuring all data initiatives align with the broader Risk strategy and business priorities.

Define and maintain a clear roadmap and planning for all analytical lines of work, ensuring feasibility, sequencing, and delivery commitments.

Stakeholder & Product Collaboration

Work closely with Product Owners and key stakeholders across Risk, Collections, Engineering, and Architecture.

Understand the functionality and business logic behind each line of work to design technically sound and business-aligned solutions.

Communicate progress, insights, risks, and recommendations clearly to both technical and non-technical audiences.

Technical Excellence & Solution Design

Design and lead the end-to-end execution of advanced ML solutions, including model definition, experimentation strategy, architecture of the pipeline, and production deployment.

Create high-level and detailed solution designs , making key decisions on algorithms, architecture, features, evaluation, and scalability.

Drive forward-looking analytical practices such as causal inference, conformal prediction, explainability, fairness, and uncertainty modeling.

Hands-on Development & Model Oversight

Guide (and when needed, contribute hands-on to) the development of models using our analytical stack: XGBoost, CatBoost, causal inference frameworks, conformal prediction, traditional ML and statistical modeling, etc.

Oversee the lifecycle of ML products: feature engineering, validation, testing, deployment, monitoring, and continuous improvement.

Ensure models are production-ready, efficient, and compliant with regulatory and governance standards.

Team Coordination

Coordinate and mentor Data Scientists, ML Engineers, and Data Engineers.

Enable high-performing, collaborative teams through guidance, feedback, and technical direction.

Required Qualifications
Experience

6+ years of experience in Data Science, Machine Learning, or AI developing end-to-end ML solutions (minimum requirement).

Proven experience leading analytical initiatives and collaborating with cross-functional teams.

Experience in credit risk, collections, or financial services is a strong plus.

Technical Skills

Strong proficiency in Python , SQL , ML frameworks (scikit-learn, PyTorch, TensorFlow), and distributed processing (PySpark).

Strong knowledge of ML operations: pipeline design, monitoring, drift detection, retraining, CI/CD for ML.

Experience working in cloud environments (AWS, GCP, Azure).

Familiarity with explainable ML, fairness, uncertainty and governance practices.

Soft Skills

Excellent communication skills to interact with stakeholders, PO, and leadership.

Ability to translate business needs into analytical solutions.

Strong planning and organizational abilities; comfortable managing several lines of work simultaneously.

Adaptability and resilience in fast-paced, evolving environments.

Leadership presence and the ability to guide and mentor multidisciplinary teams.

Skills:

Customer Targeting, Empathy, Ethics, Innovation, Proactive Thinking