Data Scientist with Python and R

- Identify and evaluate predictive modeling opportunities across various business dimensions, including customer engagement and operations. - Shape the modeling roadmap based on business impact, feasibility, data accessibility, and company priorities. - Collaborate with stakeholders from Strategy Analytics, Product, Marketing, and Finance to convert ambiguous business queries into actionable data science initiatives. - Ensure that predictive projects align with measurable business objectives, emphasizing practical outcomes over technical pursuits. - Design, construct, validate, and enhance predictive models that positively influence decision-making throughout the organization. - Utilize appropriate methodologies such as classification, regression, forecasting, and machine learning to tackle specific challenges. - Develop strategies for various use cases, including customer retention, demand forecasting, and prioritization workflows. - Establish success metrics and validation frameworks that assess model performance alongside business applicability. - Collaborate on deploying findings to integrate model outcomes into operational processes and reporting systems. - Lead modeling efforts across distributed teams without direct managerial responsibilities, ensuring clarity in priorities and project deliverables. - Foster a robust operational cadence that includes project planning, execution, review, and continuous enhancement of analytical practices. - Set best practices for feature creation, model documentation, and performance monitoring to elevate analytics standards. - Collaborate with data engineering teams to enhance ML-ready datasets and improve data activation processes. - Effectively communicate modeling insights and recommendations to both technical and non-technical stakeholders, emphasizing the implications for business actions.