Senior Manager, Data Analytics

Lead Data and Analytics for Payment Performance

We pride ourselves on being the best payment platform for achieving market leading acceptance rates through our Intelligent optimisation and fraud detection engine.

As Senior Data Analytics Manager, you will own the data analytics delivery underpinning multiple products that define our competitive edge. You will provide direction for product success through insights, build data products that measure success and ROI for our payment performance engine. You will manage a team of Product Data Scientists and Analytics Engineers.

• Leadership: Own the data and analytics strategy for the product vertical, end-to-end delivery of insights and data products.
• Product Measurement & Insights: Working closely with pillar leaders you will set the bar for how data is used in product decision making and measuring the ROI of the payment performance product suite.
• Data Architecture: Gatekeeper for designing the right data architecture to create clean, cost efficient and reliable data pipelines that deliver high data quality and rich data instrumentation.
• Team Development: Build and develop a high performing data science and analytics engineering team.

• Demonstrable multi year experience in leading data science, product analytics and/or data engineering teams.
• Proven track record in implementing measurement systems for product and system development, applying experimentation, causal inference and so forth.
• Experience in collaborating cross functionally with all levels of stakeholders, setting roadmaps, owning prioritization, and scoping for delivery.
• AI forward mindset and fluency in applying Gen AI in both delivery productivity as well as delivering data and analytics products.
• Demonstrable track record in managing and developing product data scientists and data engineers.
• Core analytics skills: data interrogation skills with SQL/Python, data modelling/engineering, applied statistics (e.g., hypothesis testing, regression, predictive modelling).
• Ideally, having worked with acceptance, fraud and cost optimisation within the context of payments or financial technology services.
• Robust understanding of how products should be instrumented, with the ability to optimise for rigor while considering cost and quality.