ROLE OVERVIEW
We are seeking a highly skilled and delivery-oriented Assistant Vice President to join our Enterprise Resiliency organization. This role sits at the intersection of solution engineering, data analytics, and applied artificial intelligence — serving as a critical bridge between business stakeholders and advanced technology capabilities.
The AVP will lead the design and delivery of scalable solutions, with a focus on Python-based development, Microsoft Power Platform, Power BI, and AI-powered tooling. The successful candidate will bring a consultative mindset, deep technical expertise, and the organizational maturity expected at a senior individual contributor level within a regulated financial institution.
KEY RESPONSIBILITIES
Solution Design & Delivery
Lead end-to-end design and delivery of technology solutions aligned to business objectives and regulatory requirements.
Translate complex business requirements into scalable, maintainable technical architectures.
Own delivery outcomes across multiple concurrent workstreams, ensuring quality, timeliness, and stakeholder alignment.
AI & Machine Learning Application
Architect and implement applied AI and machine learning solutions — including AI agents and automation workflows — to solve real business problems.
Evaluate emerging AI tools and frameworks for practical applicability within a regulated financial environment.
Partner with teams to move prototypes into production-grade systems.
Data Analytics & Visualization
Develop and maintain Power BI dashboards and reporting infrastructure that deliver actionable insight to senior stakeholders.
Define data models and analytics frameworks in support of business intelligence and performance management initiatives.
Stakeholder Engagement & Leadership
Serve as a trusted technical advisor to business and technology leaders; facilitate workshops and solution reviews.
Communicate complex technical concepts clearly and persuasively to non-technical audiences including executive stakeholders.
Mentor junior team members and contribute to the development of team capabilities and best practices.
REQUIRED QUALIFICATIONS
5+ years of experience in solution engineering, technical consulting, or a related role within enterprise or financial services environments.
Demonstrated ability to own and deliver complex technology solutions from discovery through deployment.
Strong proficiency in Python for data engineering, automation, and application development.
Hands-on experience with Microsoft Power Platform, including Power Apps, Power Automate, and Dataverse.
Proven expertise in Power BI, including advanced data modeling, DAX, and executive-facing dashboard design.
Practical, non-research experience with AI and machine learning — including design or deployment of AI agents, LLM integrations, or ML pipelines.
Excellent interpersonal and communication skills; track record of effective stakeholder management across technical and business functions.
Degree in Computer Science, Engineering, Information Systems, or a related quantitative discipline.
PREFERRED QUALIFICATIONS
Experience with in a regulated financial services environment (banking, asset management, insurance, or capital markets).
Familiarity with Microsoft Azure services, AWS Services, Microsoft Fabric.
Experience with Agile or hybrid delivery methodologies.
Degree in a relevant field.
Relevant certifications such as Microsoft Certified:
Power Platform Solution Architect, Azure AI Engineer Associate, or equivalent.
TECHNICAL SKILLS
Core Languages & Frameworks
Python (pandas, Num Py, scikit-learn, FastAPI, Lang Chain or equivalent)
SQL — advanced query design, optimization, and data modeling
REST APIs and JSON/XML data interchange
Microsoft Ecosystem
Microsoft Fabric— data modeling, DAX, Power Query, and report deployment
Power Platform — Power Apps, Power Automate, Dataverse, and connectors
Microsoft Azure — Azure OpenAI Service, Azure ML, Synapse Analytics
AI & Machine Learning
Applied machine learning — classification, regression, clustering, and forecasting
AI agent design and orchestration (e.g., Auto Gen, Lang Graph, Semantic Kernel, or equivalent frameworks)
Large language model…
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