Data Scientist GenAI (LLMs)

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 121000 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

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 BBVAs 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. machine learning pipeline designed to enhance early debt recovery by predicting default risk and optimizing collection strategies daily life embeddings to drive deeper personalization in customer interactions and improve service recommendations. conformal prediction to provide reliable uncertainty estimates and enhance the confidence in AI model predictions algorithmic explainability frameworks to ensure transparency and foster trust in our AI systems.

At BBVA AI Factory innovation isnt just a goal-its a continuous journey.

About the job:

At BBVA AI Factory were seeking an Data Scientist to join our dynamic team in Madrid specializing in the application of Generative AI and Large Language Models (LLMs). Our team thrives on collaboration and innovation tackling complex challenges in finance with state-of-the-art AI solutions. Whether its developing explainable algorithms building Machine Learning pipelines to improve debt recovery or leveraging LLMs for enhanced customer experiences we focus on creating real-world impact. As part of our team youll build end-to-end data products that incorporate cutting-edge AI to enhance key banking processes. Collaborating with a diverse group of experts youll help develop a new AI-supported customer relationship model that benefits both customers and managers.

Key job responsibilities

• Lead Analytical Projects: Manage and execute key analytical projects within the DATA area aligning with strategic objectives while leading analytical teams to ensure successful project delivery.

• Data Analysis: Analyze large and complex data sets to uncover trends and insights that drive business decisions.

• Model Building: Develop predictive and generative models using statistical machine learning and LLM techniques.

• LLM Lifecycle Management: Evaluate fine-tune and optimize LLM models for domain-specific applications integrating them into analytical solutions.

• Experimental Development: Lead the iterative development of LLM-based solutions including constructing knowledge bases datasets and performance evaluation methodologies.

• Cross-Functional Collaboration: Work closely with product managers engineers and designers to implement data-driven solutions and integrate LLM capabilities effectively.

• 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 all deliverables meet Advanced Analytics governance standards and best practices.

Your Qualifications

• Education: Bachelors or Masters degree in Computer Science Statistics Mathematics or a related field.

• Experience: 5 years of experience working as a Data Scientist including significant work with LLMs and multidisciplinary projects.

• LLM Expertise: Advanced technical knowledge of LLMs including fine-tuning prompt engineering embedding elicitation and model optimization.

• Programming & ML Frameworks: Deep expertise in Python statistical modeling and machine learning with strong proficiency in Pandas NumPy scikit-learn plus hands-on experience with PySpark TensorFlow PyTorch and HuggingFace Transformers; experience with libraries such as LangChain or LangGraph is a plus.

• Data Lifecycle Management: Proficiency in the end-to-end lifecycle of ML models from dataset creation and EDA to testing and monitoring/retraining.

• AI Risk Assessment: In-depth understanding of risks inherent to LLMs such as hallucination biases and unpredictability.

• 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: Excellent ability to translate complex technical concepts into actionable business insights.

• 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.

Nice to Have

• Previous experience in the financial industry.

• PhD in Computer Science Statistics Mathematics or a related field.

• Cloud Computing: Experience in AWS Google Cloud or Azure for scalable data science solutions including experience with Docker and Kubernetes.

• Experience in Causality: Knowledge and application of causal inference methods to identify and model cause-and-effect relationships in data.

Skills: