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BBVA is a global banking and financial services group working to modernize how people and businesses manage money. Its activities span corporate and investment banking, fintech, and digital banking, serving 71.5 million customers through technology-enabled financial solutions. With more than 121,000 employees, BBVA brings together expertise across banking, consulting, and marketing in a collaborative environment focused on innovation, digital transformation, and professional excellence.
Senior Data Scientist, Mexico City (Hybrid)
At BBVA, shape governed machine-learning solutions for a global banking environment. Establish technical architectures, evaluation standards, reusable practices, and expert guidance for data-science teams.
Description
- Define technical patterns, standards, architectures, and evaluation frameworks for Data Science solutions
- Lead or assess high-impact technical decisions across Machine Learning, graph technologies, Deep Learning, NLP, GenAI, and related specialties
- Design assessments covering model performance, quality, explainability, bias, security, limitations, and monitoring
- Review technically complex or materially risky cases, documenting findings, alternatives, decisions, and remediation actions
- Develop reusable components, libraries, guides, evaluations, and patterns, and support their adoption
- Mentor Associates and Experts while leading advanced technical sessions
- Partner with Managers, governance leaders, product teams, and business stakeholders to align value, quality, risk, and regulatory compliance
- Provide expert direction on technical decisions where established patterns do not exist
- Turn specialized knowledge into adoptable standards while maintaining experimental rigor, traceability, governance, and business alignment
Requirements
- Advanced proficiency in Python
- Deep experience with Machine Learning and model evaluation
- Demonstrable expertise in at least one area such as graph technologies, Deep Learning, NLP, GenAI, or recommendation systems
- Experience processing and analyzing data with Spark, PySpark, SQL, and large-scale data ecosystem tools
- Background in experimental design, baseline comparison, model validation, and limitation documentation
- Knowledge of model-governance practices including versioning, traceability, documentation, risk controls, and monitoring
- Ability to design architectures, technical patterns, or reusable assets and support their adoption
- Experience working in cloud environments, preferably AWS
- Experience with MLOps, CI/CD, model deployment, and model monitoring
- Knowledge of LLMs, RAG, generative-system evaluation, guardrails, and responsible AI techniques
- Participation in technical communities, publications, library contributions, or specialized training
- Bachelor’s degree in Mathematics, Engineering, Actuarial Science, Computer Science, Data Science, or a related field
- 8 to 12 years of experience in Data Science
- Evidence of applying or piloting standards, evaluations, patterns, or technical assets in at least two contexts
- Sustained experience providing technical mentoring
Benefits
- Reasonable accommodations throughout the selection process
- A collaborative and inclusive work environment
- Commitment to diversity, inclusion, and equal opportunity
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