Senior Data Scientist

Most data science roles ask you to go deep in one domain. This one asks you to go wide, and still be excellent. As a Senior Data Scientist, you'll work across Credit Risk, Growth, and AI initiatives based on where the team needs the most leverage at any given time. Some quarters that means contributing to a fraud model. Others it means owning a personalisation or growth experiment end-to-end. You'll be expected to ramp quickly, work with high autonomy, and deliver without needing a fully paved path. This role is critical to how we scale; it creates the flexibility and redundancy the team needs to execute across a wide roadmap without breaking.

Responsibilities:

• Contribute across Credit Risk (underwriting, fraud, collections), Growth (personalisation, recommendations, incentivization), and AI initiatives based on team priorities.
• Own projects end-to-end within your assigned domain: data, modelling, deployment, and post-launch monitoring.
• Develop deep familiarity across multiple domains and systems, enabling the team to move faster and maintain continuity as we scale.
• Drive research and exploration workstreams, evaluating new modelling approaches, alternative data sources, and tooling that keeps Aplazo ahead of the curve
• Collaborate closely with Lead and Staff Data Scientists to deliver on the broader roadmap.
• Identify and flag opportunities for improvement across domains; you'll often be the person with the broadest view of what's working and what isn't.

Requirements:

• 4-8 years of experience across more than one data science domain, Risk, Growth, Personalisation, or similar.
• Strong ML fundamentals; you can pick the right tool for the problem rather than defaulting to what you know best.
• Demonstrated ability to ramp quickly on unfamiliar problems and deliver with limited guidance.
• Proficiency in Python, SQL, and standard ML frameworks.
• Hands-on experience with LLMs / GenAI as part of your regular workflow.
• Production experience: you've taken models past the notebook stage.

Strong Plus:

• Fintech or BNPL background, familiarity with credit risk or fraud modelling concepts.
• Experience with experimentation frameworks and A/B testing at scale.
• Exposure to MLOps practices, feature pipelines, model monitoring, deployment.
• Publications or presentations in recognised Machine Learning and Data Science journals/conferences.