Data Scientist

Demonstrate 6+ years of experience in applied data science or product analytics, or equivalent practical experience.

Apply advanced SQL and Python (Pandas, Scikit-learn, Statsmodels, PyTorch or TensorFlow) to solve complex analytical problems.

Build, deploy, and monitor ML models in production environments with a focus on reliability and performance.

Work with telemetry and event-level data to model user behaviour and product engagement.

Use MLOps platforms such as MLflow, SageMaker, Vertex AI, or Kubeflow to manage model lifecycles.

Apply data quality and observability tools (e.g.

Designing and deploying end-to-end ML models — including PQL scoring, churn prediction, and expansion modeling — into production environments with defined latency and accuracy SLAs.

Building and maintaining MLOps pipelines covering dataset versioning, feature drift monitoring, automated retraining, and model registry management.

Collaborating with Product and Data Engineering teams to establish telemetry schemas, data contracts, and quality validation frameworks, ensuring models train and score using dependable data.

Owning model observability by creating dashboards and alerts for performance degradation, prediction drift, and data anomalies — and leading incident response when issues arise.

Standardising analytics frameworks by developing reusable dbt data models and owning the full experimentation lifecycle, from A/B test design to ship/no-ship recommendations.

We build data products that power revenue decisions across a multi-product SaaS platform. Our team partners closely with Sales, Product, and Engineering to turn telemetry into action.