Overview
The Associate Senior Data Scientist leads the extraction of meaningful insights from data assets to support informed organizational decision-making. This role leads the design and execution of data governance and analytics initiatives, manages collaboration with stakeholders, and leads the development of statistical models, predictive models and machine learning techniques to solve complex business problems.
Key Responsibilities
- Manages analytical and advisory services or operational work in core areas of data science.
- Leads the development of a data governance framework that includes clear guidelines for data classification, ownership, and stewardship.
- Leverages statistical concepts, practice, and theory, and knowledge of probabilities to guide clients and/or stakeholders in identifying key operational issues and optional solutions.
- Designs and implements data, research and evidence-based solutions to bank-wide institutional directions and strategic vision.
- Leads collaboration to develop data solutions and market knowledge and ensure continuous processes improvements.
- Manages the development of data models and oversees data processing and cleaning, ensuring effective quality assurance and data visualization.
- Leads the development of machine learning and AI tools to accelerate reasoning and analysis to support decision making.
- Translates technical analyses in core areas of data science into recommendations for programs, policies, and strategies for social and economic development.
- Manages the adoption and suggestion of new concepts and ideas to from inside and outside the organization to optimize data solutions.
- Experience with data design, data visualization, data presentation, and data storytelling using design thinking approaches.
- Design of mobile apps for just in time and in the flow of work insights for decision making.
- Knowledge of corporate data domains, including budget and human resources.
Required Experience
- Master’s degree and 8+ years progressively responsible experience; or Bachelor’s with 10+ years; or equivalent combination of education and experience.
- Proven experience managing global digital engagement platforms and solutions.
- Demonstrated mastery in developing and deploying statistical models, predictive analytics, and machine learning algorithms (including supervised/unsupervised learning, regression, classification, and neural networks) that solve complex business problems, with deep knowledge of probability distributions, hypothesis testing, and experimental design applied to real-world organizational challenges.
- Demonstrated experience in data design, visualization, presentation, and storytelling, applying design thinking to translate complex analysis into clear, decision-oriented insights, including the design of mobile applications that deliver just-in-time, in-the-flow-of-work insights for rapid decision making.
- Knowledge of corporate data domains—particularly budget and human resources—and the ability to leverage these datasets to develop actionable analytics and visual products.
- Proven experience designing enterprise-wide data governance frameworks that support reproducible research and model validation, including protocols for feature engineering, model versioning, data lineage tracking, and ethical AI practices that ensure data quality, bias mitigation, and compliance across analytical workflows.
- Ability to distill complex statistical analyses, A/B test results, and machine learning model outputs into compelling data narratives for executive leadership, translating mathematical insights and model performance metrics into clear business strategies, evidence-based recommendations, and operational improvements that drive organizational impact.
- Track record of building complete analytical pipelines from data exploration and feature selection through model development, validation, and production deployment, delivering interactive dashboards, automated reporting systems, and self-service analytics tools that democratize insights across the organization.
- Demonstrated ability to mentor junior data scientists in advanced techniques (ensemble methods, deep learning, NLP, time series forecasting) while evaluating and championing emerging tools and methodologies from academic research and industry that enhance analytical capabilities and accelerate insight generation.
