Lead Data Analyst

We’re looking for a Lead Data Analyst who is passionate about our mission of Money Without Borders to partner with our operational teams to help drive data-driven decisions that would support our fast-growing product through scaling and optimising the team.

As a Lead Data Analyst, you'll be driving our analytics efforts in our operations teams, who do everything from support our customers when they need help, to screening for criminal activity, to verifying customer identities at scale. 

Most importantly, you’ll collaborate closely with your operational leads, as well as workforce management, quality, training, and knowledge management, to bring your insights into real change for our customers and help drive our mission!

Here’s how you will be contributing:

• 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/FinCrime 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.

This is an IC3 role. For more information on our Analytics Career Map and levelling structure, click here.

What you will bring:

• Quantitative Foundation. Ideally, you have a background in statistics, maths, physics, engineering, economics, or another scientific field. You apply first-principles thinking to break down complex operational problems.

• A Statistical Mindset. You have a natural grasp of data logic. You don't just look at averages; you understand distributions, variance, and significance. You enjoy applying that rigor to messy, real-world operational data to separate signal from noise.

• You have 4+ years of experience in analytics with demonstrated ability to approach complex problems with a strategic mindset, identifying innovative solutions that drive operational improvements.

• You have a background working with operational team analytics including capacity planning, forecasting, efficiency analysis, quality assurance, predictive analytics, and experimentation.

• You have experience working with complex data models in SQL (our warehouse is Snowflake) and analysing it using advanced SQL/Python/R

• Able to demonstrate that you can tell a  story and proactively give guidance on strategy based on insights

• You have experience with data visualisation tools (Looker, PowerBI, Tableau etc.) and demonstrate storytelling ability with data

• You have a bias to action - you identify what needs to be done and make it happen.

Some extra skills that are great (but not essential): 

• Prior experience in Operation domains

• You have experience working with a WFM or Quality team.

• You have experience working with WFM Systems

• You have experience with forecasting techniques such as ARIMA, Holt-Winters, and other time series analysis methods