About the company
Wise is a global technology company, building the best way to move and manage the world’s money. Min fees. Max ease. Full speed.
Responsibilities
- Analytical Capacity Planning and Forecasting: Focus on building and refining analytical models for strategic capacity planning. Take ownership of forecasting efforts to align with business growth and operational demands.
- Data Pipeline Ownership: Take ownership of data pipelines to maintain and improve data flow, ensuring reliability and accuracy.
- Predictive Modeling and Cause and Effect Analysis: Develop and implement robust models to predict outcomes and perform cause and effect analysis to identify key drivers, optimise processes, and enhance decision-making and strategic planning.
- Strategic Support and Analysis: Provide critical insights to assess the operational health of the KYC function, conduct in-depth cost analysis, and offer detailed analysis of operational metrics (including quality) to understand impacts on customer experiences.
- Performance Tracking and Initiative Optimisation: Monitor and track the performance of key strategic initiatives, capitalising on optimisation opportunities to enhance operational outcomes.
- KPI Implementation and Target Setting: Lead the development and implementation of the operations KPI tree and the target-setting framework, integrating these within reporting pipelines and strategic operations.
- Stakeholder Collaboration and Process Standardisation: Collaborate closely with various stakeholders to standardise processes across forecasting, scheduling, and real-time operations, promoting continuous improvement and strategic alignment.
Requirements
- 4+ years of experience in analytics with a demonstrated ability to approach complex, interconnected problems with a systems mindset.
- Strong quantitative and statistical background, comfortable with probability, hypothesis testing, regression, time series analysis, and thinking in terms of models and trade-offs.
- Experience with operational analytics: efficiency analysis, cost modelling, performance measurement, or process optimisation.
- Think about data as infrastructure. Build with reusability and quality in mind, considering how data will be consumed by dashboards, other teams, and automated systems.
- Experience with complex data models in SQL (warehouse is Snowflake) and advanced analysis using Python/R.
- Ability to tell a story with data and proactively guide strategy based on insights. Experience with visualisation tools like Looker, Superset, or Tableau.
- Bias to action and excellent communication skills, able to own projects end-to-end.