Staff Data Scientist

The Staff Data Scientist will provide technical leadership for enterprise wide capabilities in data science, AI and predictive analytics, and lead data science projects to identify opportunities where predictive analytics, machine learning, or generative AI can improve productivity, reduce cost, or unlock new capabilities.

Requirements

• Identifying High Value Analytics & AI Opportunities
• Partner with business leaders to identify opportunities where predictive analytics, machine learning, or generative AI can improve productivity, reduce cost, or unlock new capabilities.
• Develop clear business cases and ROI models to prioritize initiatives and communicate value to senior leadership.
• Translate complex business requirements into robust, scalable technical solutions.
• Select and implement appropriate modeling techniques, including classical ML, deep learning, generative AI, and reinforcement learning where applicable.
• Oversee the full model lifecycle: data exploration, feature engineering, model development, evaluation, deployment, monitoring, and continuous improvement.
• Ensure solutions are production ready, maintainable, and aligned with MLOps best practices.
• Drive organization wide adoption of models and AI systems through clear communication, documentation, and stakeholder engagement.
• Provide expert consultation on ML algorithms, model tuning, experimentation frameworks, and cloud native data engineering patterns.
• Mentor data scientists, ML engineers and AI engineers; support skill development in areas such as forecasting, ML modeling, generative AI, vector databases, and modern ETL/ELT workflows.
• Contribute to the development of internal standards, reusable components, and best practice guidelines.
• Develop and maintain project plans, milestones, and communication strategies for strategic initiatives.
• Facilitate regular updates with stakeholders, executives, and cross functional partners.
• Coordinate with vendors, consultants, and technology partners when external expertise is required
• Evaluate emerging technologies including generative AI platforms, MLOps tools, cloud services, and data engineering frameworks to determine applicability and business value.
• Recommend and influence adoption of modern, flexible, and scalable technologies that support a unified enterprise data and AI platform.
• Drive experimentation and prototyping to accelerate innovation and reduce time to value.