Our client’s success is our success. And you make it happen 
 
Payment systems are complex, regulated and ever-changing. And, while we may be small, we are at the forefront of innovation punching above our weight. We’re enabling the future for our clients through tech like digital payment solutions, the New Payment Platform (NPP), buy now, pay later, and open banking.  
 
Our product teams are the eyes and ears of Cuscal. They’re client-focused relationship managers, serving everything from business development, marketing, fraud, and customer support. They work out what clients need, so we can build it and launch it… often, in months, not years.  
 
We are looking for a Data Scientist to join our Financial Crime Team and help advance our Financial Crimes monitoring program  

As payment methods evolve, so too do the efforts of criminals in their attempts to defraud businesses and consumers.  From sophisticated online scams to identity theft and phishing. We’re looking for a Data Scientist to continue to drive forward Cuscal’s robust fraud monitoring solutions that leverage artificial intelligence (AI) and Machine Learning (ML) to stay ahead of emerging fraud trends, while protecting our clients; their customers, brand, and balance sheets.  

Reporting to the Senior Data Scientist, the Data Scientist will identify, prioritise and undertake a range of research and analytical activities leveraging all available data assets to help solve complex business problems.  

About the Role: 

1) Data Science Strategy and Planning: 

• Develop Cuscal fraud detection and prevention strategies to stay ahead of emerging fraud trends and evolving threats.  

• Formulate a coherent cross-business approach and strategic/tactical plan for big data initiatives. 

2) Data Analysis and Modelling 

• Conduct proactive, in-depth analysis of multiple data sets to prevent high-risk and financial crimes activity and emerging threats or optimize current monitoring across multiple channels, including cards, 3D secure, digital wallets, real-time payments, and PayTo.  

• Develop and optimise predictive modelling software programs, algorithms, and automated processes to cleanse, integrate, and evaluate large data sets from multiple disparate sources. 

• Perform deep-dive analyses of experiments through reliable modelling methods that include numerous explanatory variables and covariates.  

• Translate analytical insights into concrete, actionable business, process, or product improvement recommendations. 

3) Continuous Improvement: 

• Deliver continuous improvement activities to prevent threats while optimizing customer experience and operational outcomes, including improvements to identify undetected fraud.  

• Ensure all processes for data analysis, machine learning models, and model governance are aligned to Cuscal’s AI strategy, including best practice guidelines for instrumentation and experimentation. 

• Adopt data science best practices to deliver quantitative improvements to the analytics and process modelling functions.

About You: 

• University qualification in data science, Computer Science, Statistics, or a related field. Industry certifications related to data science and fraud detection highly regarded  

• Demonstrated experience working with tools that deploy fraud rules and Machine Learning algorithms. 

• Financial Services experience in payments, Card payments, Acquiring, 3D secure, Digital banking and real-time payments. Experience in fraud and financial crimes is desirable.   

• Understanding of relational databases and programming, including SQL, R, and Python 

• Experience interpreting data from visualisation tools such as PowerBI and visualisation libraries such as Matplotlib, Seaborn, and Plotly. 

• Data mining of large data sets to identify emerging patterns, trends and opportunities. 

• Knowledge of Natural Language Processing and Large Language Models is desirable.  

Join us at the forefront of payments 
   
Why Cuscal? 
We are in the rapidly evolving world of payments, and we are committed to providing a diverse and inclusive workplace where the very best talent in Australia chooses to work. We support our colleagues with flexible work arrangements through our hybrid model whilst also offering a wide range of educational, financial, lifestyle, health & wellbeing benefits. 
 
Next Step 
If you think this role is the right fit for you, we invite you to apply. Let’s explore who you are and what drives you. We’d love to share our vision for the future of the payments sector. Please note candidate screening and interviews may be conducted prior to the closing date of the job advert. 
     
Cuscal does not accept unsolicited resumes from recruitment agencies and search firms. Please do not email or send unsolicited resumes to any Cuscal employee, location or address. 

The Reporting and Data Analyst plays a crucial role by transforming raw data into meaningful insights that drive decision-making. The primary responsibilities include collecting, analysing, and interpreting data from various sources. They create detailed reports, visualisations, and storytelling to highlight key metrics and trends, making complex data more accessible and understandable for stakeholders. This role often involves working with tools like SQL, Excel, and data visualisation software such as Tableau and/or Power BI.

In addition to reporting, data analysts delve deeper into the data to uncover patterns and insights that can inform strategic decisions. They perform tasks such as data cleaning, data mining, statistical analysis, and predictive modelling. By interpreting data within the context of business objectives, they help to identify opportunities for improvement, optimise
operations, and predict future trends, while maintaining a focus on data governance and quality. This dual focus on reporting and analysis ensures that businesses not only understand their current performance but also have business insights needed to plan and document.

A Reporting and Data Analyst in data and analytics typically has several key accountabilities:

Data Governance

• Understanding of data policies, standards, and regulations.

Data Quality, Integration and Management

• Data Mining: Skills in extracting patterns and insights from large datasets.
• Data Collection: Gather data from various sources, including databases, APIs, and flat files.
• Data Cleaning: Ensure data quality by cleaning and preprocessing data to remove inconsistencies and errors.
• Data Warehousing: Design and maintain data warehouses to store and organise large volumes of data.

Data Architecture

• Knowledge of data structures, databases, and storage solutions.
• BI Solutions: Develop and implement BI solutions, including dashboards, reports, and data visualisations.

Reporting and Visualisation

• Dashboard Creation: Create interactive and user-friendly dashboards using tools like Power BI and/or Tableau.
• Data Storytelling: the ability to communicate your insights through compelling narratives.
• Report Generation: Develop reports that provide actionable insights to stakeholders, helping them make informed decisions.
• Data Visualisation: Use data visualisation techniques to present complex data in an easily understandable format.

Agile Practices

• Demonstrate the success of a product release, encompassing a shared understand of finished quality product.

Drives Results

• Organises workload and priorities to adhere to agreed timeframes and service standards.
• Explores alternative solutions to improve effectiveness and productivity within the team.
• Makes informed and timely decisions when appropriate, weighing up risks and outcomes using sound risk awareness and management practices.