Data Scientist GenAI (LLMs)

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BBVA es una compañía global con más de 160 años de historia que opera en más de 25 países donde damos servicio a más de 80 millones de clientes. Somos más de 121.000 profesionales trabajando en equipos multidisciplinares con perfiles tan diversos como financieros, expertos legales, científicos de datos, desarrolladores, ingenieros y diseñadores.

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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 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. [https://www.bbvaaifactory.com/category/mercury/]

• A machine learning pipeline designed to enhance early debt recovery by predicting default risk and optimizing collection strategies [https://www.bbvaaifactory.com/a-machine-learning-pipeline-for-early-debt-recovery-executive-summary/]

• Applying daily life embeddings to drive deeper personalization in customer interactions and improve service recommendations. [https://www.bbvaaifactory.com/behind-daily-life-embeddings-in-action/]

• Utilizing conformal prediction to provide reliable uncertainty estimates and enhance the confidence in AI model predictions [https://www.bbvaaifactory.com/conformal-prediction-an-introduction-to-measuring-uncertainty/]

• Building algorithmic explainability frameworks to ensure transparency and foster trust in our AI systems. [https://www.bbvaaifactory.com/algorithmic-explainability-how-do-we-apply-it-in-bbva-ai-factory/ ]

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

Sobre el puesto

At BBVA AI Factory, we're 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 it’s 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, you'll build end-to-end data products that incorporate cutting-edge AI to enhance key banking processes. Collaborating with a diverse group of experts, you'll 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: Bachelor's or Master's 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

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