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VP Data Analytics (Banking)
- United States
- United States
Über
Located on-site in Manhattan Base salary $110k-200k depending on skills and experience Requirements: Bachelor's degree required; Master's degree in computer science/statistics/applied analytics/business analytics, or other analytics-relevant majors preferred At least 5 years of experience in data analytics, SQL, Python, and Business Intelligence Tools At least 3 years of experience in dashboard development At least 2 years of experience in Machine Learning/AI Good communication skills (written and verbal) and project management experience required Financial Services Experience Required, Banking experience preferred Strong analytical, machine learning and Artificial Intelligence (AI), decision-making and problem-solving skills Responsibilities: BI Dashboard/Executive Dashboard Development:
Leverage Business Intelligence tools to design and build interactive dashboards. Develop clear, insightful visualizations that track key performance and risk indicators, diagnose issues, surface data-driven solutions, and empower stakeholders to make informed, timely decisions. In-depth Analysis:
Perform comprehensive analysis of complex datasets to identify trends, patterns, and anomalies. Translate findings into actionable insights that support data-enabled decision-making and drive continuous improvement across the organization. Advanced Analytics Including Machine Learning/AI:
Identify high-value opportunities to apply advanced analytics, including Machine Learning and AI, to solve business challenges, improve performance, and mitigate risks. Develop predictive models and other advanced analytical solutions to uncover deeper insights and support strategic initiatives. Support experimentation, A/B testing, and model performance monitoring. Automation:
Support the design and implementation of automated processes to streamline workflows and reporting. Improve operational efficiency, reduce manual effort, and ensure consistent, accurate, and timely data delivery through automation. Stakeholder Collaboration:
Gather and evaluate specific data requirements from business stakeholders, including executive members, propose tailored data solutions through analytics, and effectively communicate these solutions. Presentation and Communication:
Create effective presentation decks to present data analysis results to stakeholders and management. Advocacy and Education:
Promote data analytics best practices and technology adoption within the organization.
Sprachkenntnisse
- English
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