SVP, Applied AI

Job Summary

We are seeking a visionary and execution-focused SVP to lead our Applied AI and Data Science function. This role will drive the strategic identification, design, and delivery of high-impact AI initiatives across the enterprise, while also overseeing our data science team to form a unified AI/Data Science capability. The SVP will be responsible for transforming business operations through applied AI innovation, including conversational AI, intelligent automation, and decisioning systems, with a strong focus on measurable outcomes and business impact.

This role is hybrid and requires working onsite three days a week working in either our Charlotte, NC office or our Norfolk, VA office.

Qualifications

• 10+ years of experience in AI, ML/data science, or product development, with 5+ years in leadership roles.
• The ideal candidate has previous experience working for a Fintech, Big Tech or AI Solutions Product company.
• Proven track record of delivering AI solutions in financial services or regulated industries.
• Deep expertise in AI/ML technologies such as NLP, computer vision, predictive analytics, and intelligent automation.
• Strong understanding of AI ecosystems including LLM orchestration, voice AI, decision engines, and workflow automation.
• Experience with platforms such as Azure Cognitive Services, Dialogflow, or equivalent.
• Proficiency in Python, R, SQL, and modern data science tools and frameworks.
• Demonstrated success in managing cross-functional teams and delivering operational AI solutions at scale.
• Bachelor's in Computer Science, Engineering, or related field; advanced degree preferred.

Preferred Qualifications

• Experience in consumer collections, legal recovery, or dispute resolution.
• Familiarity with AI governance, responsible AI practices, and model risk management.

Key Responsibilities

AI Strategy & Execution

• Lead the enterprise-wide AI strategy focused on applied innovation and business transformation.
• Identify and prioritize AI opportunities across business areas including underwriting, call center, digital, legal, and compliance.
• Translate operational challenges into structured AI use cases and deliver scalable solutions.
• Oversee deployment of AI agents (voice/text), document automation, intelligent routing, and LLM-based solutions.
• Define and monitor KPIs including automation rate, recovery uplift, resolution time, and model performance.

Data Science Leadership

• Integrate and lead the data science function, aligning it with AI strategy to deliver predictive models, optimization, and analytics.
• Guide teams in developing and operationalizing ML models, statistical analysis, and experimentation.
• Ensure robust model governance, monitoring, and compliance with regulatory frameworks (e.g., Reg F, CCPA).

Cross-Functional Collaboration

• Communicate AI strategy and impact to executive stakeholders, articulating ROI and strategic value.
• Partner with Investments, Ops, Tech, Risk / Compliance, Finance and external vendors to deliver AI solutions that are secure, ethical, and compliant.
• Foster a culture of innovation, experimentation, and continuous improvement across AI and data science teams.

Leadership/Supervisory Responsibilities & Level

• Lead AI and Data Science team and help to improve business focus and outcomes through development of world class impact-oriented talent.
• Influence internal and external stakeholders as a thought leader in applied AI and data science.
• Develop and track progress against a product roadmap for AI and data science capabilities.
• Define success metrics for team members and projects, ensuring clarity of expectations and accountability.
• Own resolution of operational issues, process improvements, and cross-functional impediments.
• Provide mentorship and coaching to foster technical and professional development across the team.
• Ensure compliance with applicable laws, regulations, and internal policies during design and implementation of AI/ML solutions.
• Support budget planning, resource allocation, and performance management for the function.
• Exposure to compliance environments and regulatory frameworks in financial services.

Success Metrics

• Business impact from AI initiatives (e.g., collections uplift, efficiency gains).
• Time-to-value for AI deployments from concept to scale.
• Adoption and utilization of AI tools across business units.
• Model performance and reliability aligned with business goals.
• Compliance with internal and external AI governance standards.
• Innovation pipeline of validated AI use cases.
• Stakeholder satisfaction and strategic alignment.
• Cost efficiency and ROI of AI investments.