Data and Analytics Lead

· High‑growth SME non‑bank lender

· Own our automated credit decisioning and data platform

· Hands‑on “player‑coach” role with future team growth

We have an exciting opportunity for a hands‑on Data & Analytics Lead to join Banjo, a leading SME non‑bank lender based in the Melbourne CBD, and own the data, analytics and automated credit decisioning capability that underpins our growth in the unsecured SME segment.

The Company

Banjo is a dynamic online lender providing Business Unsecured Lines of Credit, Business Term Loans and Commercial Asset Finance facilities to small and medium sized Australian businesses. We combine modern technology, data‑driven decisioning and personal service to deliver fast, practical funding solutions that help Australian SMEs grow.

The Role

Reporting to the CFO, this role will own Banjo’s data and analytics capability end‑to‑end, with a particular focus on our Azure/Databricks‑based automated credit assessment platform. You will be responsible for evolving our credit decisioning models and automation boundaries, building out a scalable data platform and warehouse, and enabling a centralised reporting and analytics model across the business.

You will be both strategic and hands‑on: comfortable setting data and model strategy, but also writing code, building pipelines, and shipping improvements yourself. Over time, you will help shape and lead a small data team as our needs grow.

This is a key strategic role in a small, high‑performing organisation, suited to someone who enjoys being close to the detail while having a material impact on credit outcomes and business performance.

Key Responsibilities

Automated credit assessment & risk architecture

• Own and evolve Banjo’s automated credit assessment capability on Azure/Databricks, including decisioning logic, scoring model usage and serviceability modelling.
• Define and maintain risk thresholds, automation boundaries and override governance frameworks that balance growth, risk and operational efficiency.
• Partner with Credit and Risk to ensure models and decisioning align with credit policy, risk appetite and regulatory expectations.

Data, models and quantitative analytics

• Lead the development, deployment and ongoing monitoring of credit risk and other predictive models (e.g. scorecards, behavioural/usage models, propensity models).
• Work hands‑on in Python and SQL to build and maintain robust, production‑grade model pipelines and features.
• Implement and manage ML lifecycle practices (e.g. experiment tracking, model registry, performance monitoring) to ensure models remain accurate and well‑governed.

Data platform, engineering and warehouse

• Design and build a scalable data platform and warehouse to support centralised reporting and analytics across the business.
• Develop and maintain reliable, high‑quality data pipelines, including data quality frameworks, observability and monitoring.
• Ensure appropriate access control, data security and privacy practices are embedded in all data solutions.

Reporting, analytics and stakeholder enablement

• Establish and maintain a single source of truth for core metrics and reporting across Sales, Finance, Marketing, Credit and Operations.
• Partner with stakeholders to understand their reporting and analytics needs and translate them into robust, self‑service data models and dashboards.
• Support Board and Executive reporting with accurate, timely and insightful data and analysis.

Leadership, collaboration and team growth

• Act as the organisation’s primary data and analytics leader and subject‑matter expert.
• Collaborate closely with Technology, Product, Credit, Finance and other teams to ensure data and models are deeply integrated into decision‑making and product roadmaps.
• Over time, help hire, mentor and lead a small data team (e.g. data engineer and/or data analyst) while maintaining a strong hands‑on contribution yourself.

About You

· 5+ years’ experience across data science, quantitative modelling and/or data engineering, preferably in financial services, lending or fintech.

· Strong experience in credit risk or related quantitative domains, ideally including unsecured SME lending, serviceability modelling and credit scorecards.

· Advanced Python and SQL skills, with a track record of deploying models into production and managing their lifecycle.

· Hands‑on experience with modern data platforms, ideally Azure and Databricks, Spark (PySpark / Spark SQL), distributed data systems and performance tuning.

· Experience designing and operating robust data pipelines, including CI/CD, version control (Git) and data quality/observability practices.

· Comfortable working directly with senior stakeholders (including Exec and Board‑level reporting) and explaining complex data and model concepts in plain language.

· You thrive in small, fast‑paced environments, are highly hands‑on, and enjoy wearing multiple hats (data science, engineering, analytics, governance).

· You’re pragmatic – able to balance analytical rigour with delivery speed and commercial impact.

Qualifications

· Bachelor’s degree in a quantitative field such as Statistics, Mathematics, Computer Science, Engineering, Economics or similar.

· Postgraduate qualification and/or relevant certifications in data science, analytics, or cloud/data engineering are a plus, but not essential.

To Apply

If you are excited by the opportunity to own and build the data and automated decisioning capability at the heart of a growing SME lender, and you enjoy being both strategic and hands‑on, we’d love to hear from you.