This is an applied, product-facing data science role spanning metric design, modelling, experimentation and production integration. You will work across Product, Engineering, Customer Success and Commercial teams to move ideas from prototype to reliable product capability., * Develop product components using customer and marketing signals, including propensity, uplift and emerging generative-AI approaches.

• Design experimentation and measurement foundations that make product impact clear and trustworthy.
• Build durable model and data workflows with appropriate monitoring, quality controls and documentation.
• Partner with engineers to move Python and SQL work from notebooks into production services and pipelines.
• Explain technical results in practical terms that product and commercial stakeholders can act on.

• Typically four or more years in applied data science for SaaS, consumer products or marketing technology.
• Hands-on feature engineering, model evaluation, monitoring and propensity or uplift modelling experience.
• Strong Python and SQL, plus practical understanding of event schemas, orchestration and data-quality controls.
• Comfort operating across ambiguous product problems and taking work through to production.

Experience with agentic LLM applications, graph modelling, causal inference, dbt, Airflow or AWS would be useful rather than essential.

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