Senior Applied Data Scientist / ML Engineer

What You'll Do:

• Own the outcome from ambiguous customer problem to measurable impact, including building data when it doesn't exist and shipping models into real products.
• Make the value legible by ensuring predictions are understandable, trustworthy, and actionable, and report realised impact back to the business.
• Deliver end-to-end by operating deployment, testing, versioning, monitoring, and drift detection, and embed with product teams to put ML inside real workflows.

Who We Are:

• You measure your work by whether customers made better decisions, not by whether the model shipped.
• You'd rather spend a week understanding what the data means than a week tuning a model; you know the domain is the hard part.
• You chase down the data yourself and build the pipeline if that's what delivery requires, prioritising usability and customer trust.

What You'll Bring:

• 5+ years in applied data science or ML engineering with production models, strong Python and ML libraries for tabular problems, and solid SQL expertise.
• Depth in supervised learning, forecasting, or optimisation, and experience building data pipelines in Snowflake and dbt on AWS.
• Excellent communication skills to explain technical trade-offs to product, engineering, and business stakeholders.