Data Scientist developing and implementing LLM - based agents and leveraging AI techniques to improve client value. Collaborating on project challenges in a dynamic, start - up environment at Gartner.
About the role
- Data Scientist at BBVA AI Factory focusing on AI solutions in finance. Collaborate to develop AI-driven customer relationship models and analytics to enhance banking processes.
Responsibilities
- Manage and execute key analytical projects within the DATA area, aligning with strategic objectives, while leading analytical teams to ensure successful project delivery.
- Analyze large and complex data sets to uncover trends and insights that drive business decisions.
- Develop predictive models using statistical and machine learning techniques.
- Work closely with product managers, engineers, and designers to implement data-driven solutions.
- Present findings and recommendations to stakeholders across the organization.
- Guide and mentor less experienced team members to foster growth and success.
- Ensure all deliverables meet Advanced Analytics governance standards and best practices.
Requirements
- Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related field.
- 5+ years of experience working as a Data Scientist in multidisciplinary projects.
- Strong SQL skills; experience with relational databases.
- 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.
- Ability to design efficient data extraction, cleaning, and transformation processes.
- Deep knowledge of a broad set of machine learning techniques applied to solve complex business problems, including A/B testing for model performance.
- Excellent ability to translate complex technical concepts into actionable business insights.
- 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.
- Experience in AWS, Google Cloud, or Azure for scalable data science solutions, including experience with Docker and Kubernetes.
- Knowledge and application of causal inference methods to identify and model cause-and-effect relationships in data.
Benefits
- Work-life balance
- Collaboration across diverse teams
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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