Sr Data Science Engineer

• →Build and refine propensity models to support targeted sales motions, leveraging advanced machine learning techniques
• →Architect a scalable model development and deployment framework that strengthens team effectiveness and mitigates business continuity risk
• →Develop and own customer headroom models that accurately size opportunities across the book of business for specific offerings and customer segments
• →Design a comprehensive account prioritisation model to sharpen account targeting and underpin the account segmentation process
• →Implement a scalable measurement and optimisation framework for sales motions, enhancing predictive capability and driving revenue growth
• →Conduct rigorous business analysis to surface the root causes of performance gaps and deliver clear, evidence-based recommendations
• →Partner with senior stakeholders to understand strategic growth priorities and ensure all analytical solutions are aligned with business objectives
• →Develop a broad suite of models including customer segmentation via clustering, customer lifetime value modelling using survival analysis, and time-series forecasting
• →Deliver channel segmentation analysis to inform customer engagement strategy and maximise lifetime value
• →Collaborate with data engineering teams to productionise data pipelines and ensure analytical solutions scale effectively across the organisation

• →5+ years of hands-on experience with SQL for querying, cleansing, integrating, and summarising complex datasets (essential)
• →5+ years of hands-on experience with Python for data manipulation, pipeline automation, and statistical modelling (essential)
• →Proven track record of building, testing, evaluating, and iterating on revenue-generating predictive models (essential)
• →Deep expertise in core modelling techniques including XGBoost, logistic regression, random forest, and decision trees (essential)
• →Demonstrated ability to translate complex analytical outputs into clear, compelling insights for senior, non-technical audiences (essential)
• →Strong problem-solving capability and a proven ability to thrive in fast-paced environments where requirements evolve rapidly (essential)
• →Experience with Databricks (highly desirable)

Familiarity with additional modelling techniques such as k-means clustering, Kaplan-Meier survival analysis, and ARIMA (highly desirable)

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