Sr. Data Scientist, Predictive Modeling
- Lead the end-to-end development and lifecycle management of predictive and prescriptive AI/ML models, from design through deployment and continuous optimization.
- Translate business objectives into scalable data science solutions, partnering with HICV stakeholders to improve key outcomes such as conversion, engagement, and activation.
- Design and execute experimentation frameworks (e.g., A/B and multivariate testing) to evaluate impact and optimize performance against defined KPIs.
- Operationalize models into production environments, enabling real-time and batch scoring, and integrating outputs into key workflows (e.g., contact center routing, survey targeting, package activation).
- Ensure strong data governance, documentation, and model explainability, including clear definitions of inputs, features, methodologies, and outputs, while maintaining compliance with privacy and regulatory standards.
- Collaborate within a joint IHG-HICV governance model, contributing to steering committee reviews, prioritization, and ongoing performance monitoring and enhancement.
- Bachelor's or Master's degree in a quantitative field (e.g., mathematics, statistics, data science), or equivalent relevant experience
- 5-8 years of data science experience with demonstrated delivery of business impact
- Advanced proficiency in Python and SQL (including complex queries, stored procedures, and performance optimization)
- Strong experience with machine learning frameworks (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch; familiarity with LLM frameworks is a plus)
- Experience deploying models in cloud environments (e.g., GCP AI Platform/Vertex AI, AWS SageMaker)
- Expertise in causal inference, optimization techniques, and experimental design
- Experience with MLOps frameworks, including model deployment, monitoring, and lifecycle management
- Strong understanding of business impact modeling (e.g., ROI, LTV, incrementality)
- Solid grounding in statistical modeling (e.g., regression, time series, propensity modeling)
- Strong feature engineering and data preparation practices
- Strong problem-solving skills and business acumen
- Ability to communicate complex analytical concepts to non-technical stakeholders
- Attention to detail with a focus on governance and documentation
- Experience operating in cross-company or client-facing delivery models preferred
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