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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.

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At BBVA AI Factory, we operate 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. Our team thrives on collaboration and innovation, tackling complex challenges in finance with state-of-the-art AI solutions. Whether it's developing explainable algorithms or building AI pipelines to improve debt recovery, we focus on creating real-world impact.

About the job:

We are seeking a Senior Expert Data Scientist to join our dynamic team in Madrid. As a senior member of our team, you'll lead the development of end-to-end data products that incorporate cutting-edge AI to enhance key banking processes. Collaborating with a diverse group of experts, you'll drive the development of new AI-supported customer relationship models that benefit both customers and managers.

Tasks

• Lead Analytical Projects : Supervise the end-to-end development of analytical solutions, including implementation, monitoring, and model adjustments. Manage and execute key analytical projects within the DATA area, aligning with strategic objectives, while leading analytical teams to ensure successful project delivery.
  
• Translate Business KPIs into Team Tasks : Break down business objectives into actionable tasks for the team, ensuring alignment with strategic goals.
  
• Delivery Management : Oversee the product backlog and ensure timely delivery of product deliverables through committed features in every PI (Program Increment).
  
• Data Analysis : Analyze large and complex data sets to uncover trends and insights that drive business decisions.
  
• Model Building : Develop predictive models using statistical and machine learning techniques.
  
• Cross-Functional Collaboration : Work closely with product managers, engineers, and designers to implement data-driven solutions.
  
• Insight Communication : Present findings and recommendations to stakeholders across the organization.
  
• Mentorship : Guide and mentor less experienced team members to foster growth and success.
  
• Best Practices Compliance : Ensure the quality of analytical models built as well as the communication and sharing of knowledge. Coordinate with your peers to ensure that the flow of knowledge and decisions are contrasted, and identify opportunities for improvement and new ways of working.
  

Training

• Education : Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related field.
  
• A Ph D in Computer Science, Statistics, Mathematics, or a related field is a plus.
  

Specific knowledge

• Experience : 10+ years of experience working as a Data Scientist in multidisciplinary projects with at least 5 years leading multidisciplinary data science teams.
  
• Databases : Strong SQL skills; experience with relational databases.
  
• Programming : Strong skills in programming languages like Python, Py Spark, R.
  
• ETL Development : Ability to design efficient data extraction, cleaning, and transformation processes.
  
• Applied Machine Learning : Deep knowledge of a broad set of machine learning techniques applied to solve complex business problems, including A/B testing for model performance.
  
• Communication Skills : Strong communication and presentation skills. Excellent ability to translate complex technical concepts into actionable business insights.
  
• Cloud Computing : Experience in AWS, Google Cloud, or Azure for scalable data science solutions, including experience with Docker and Kubernetes.
  
• Deep Learning : Hands-on experience with deep learning techniques using frameworks like Tensor Flow, Py Torch, or Keras
  
• Expertise in Machine Learning Lifecycle : Experience in supervising the entire lifecycle of machine learning models to ensure reliable and reproducible results as data products.
  
• Ethical AI and Responsible Data Science : Knowledge of ethical AI principles, data privacy laws (like GDPR, CCPA), and a commitment to responsible data science practices.
  
• Previous experience in the financial industry is highly desirable.
  
• Experience in Causality : Knowledge and application of causal inference methods to identify and model cause-and-effect relationships would be greatly valued.
  

Skills:

Azure Machine Learning Studio, Machine Learning, Python (Programming Language), Spark SQL, Structured Query Language (SQL)