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: We're a group of people with a passion for cutting-edge technology, mutual learning, and innovative problem-solving. We strive to bring the opportunities that Artificial Intelligence offers to the financial industry. At AI Factory, we are excited to welcome a Data Scientist to our team, dedicated to harnessing the power of LLMs in the financial industry.
In this role, you will be at the forefront of managing and executing significant analytical projects within the DATA area. Your responsibilities will include:
Coordinating with team members and aligning with our strategic objectives.Solving complex problems by working closely with cross-functional teams.Identifying business opportunities and applying your data science expertise to these challenges.Ensuring that all deliverables comply with Advanced Analytics discipline governance standards and best practices.Mentoring and guiding less experienced team members. About the job:
Tasks: Definition and development of the analytical cycle of solution evaluation.Ability to assess and select appropriate LLM models for each business need.Execution of LLM test plans, including the definition, construction, and obtaining of analytical metrics (performance/cognitive, technical, and business).Capable of evaluating LLMs in both discriminative and generative modes.Refinement (fine-tuning) of LLM models on new domain data (discriminative and generative).Iterative construction of training/fine-tuning/validation datasets, evolving them based on the understanding metrics obtained and case analysis.Methodology approach for A/B testing and definition of requirements for experiments.Support for the construction and validation of KPIs.Experimental development of analytical solutions.Construction of knowledge bases for the models, e.g., domain documents, unstructured information from the vertical, etc.Iterative construction of partial or complete analytical solutions, possibly integrating inferences from different models.Integration of the product and evaluation of its performance according to methodology.Execution of sprint-by-sprint analytical experimentation cycles.Specialist support to the cross initiative.Technological prospection of advances and state of the art of current NLP (LLMs, foundational models, proprietary/open-source models, etc.).Experimentation support for specialized lines (e.g., fraud, marketing, UX, talent, and culture, etc.).
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