Corporate Vice President - AI Leader

Location Designation: Hybrid - 3 days per week 

In this role, you will collaborate with business and operational leaders to apply Artificial Intelligence (AI) and machine learning techniques to solve complex problems, generate insights, and enable data-driven strategies. You will also guide and mentor junior data scientists, providing technical leadership and career development support. Working closely with product managers, data engineers, MLOps engineers, and project managers, you will drive the delivery of innovative AI solutions that are aligned with evolving business needs. You will have a seat at the table with senior business stakeholders and are expected to champion new ideas that advance AI adoption while playing a key role in measuring and communicating business value. Success in this fast-paced environment requires staying ahead of emerging AI trends and maintaining a competitive edge.

What You’ll Do

• Provide strategic leadership across AI initiatives, driving the integration of advanced analytics and GenAI into enterprise decision-making and long-term strategy.
• Partner with the COO and embedded Tech leads to establish strategic, long-term collaboration frameworks that align functional objectives.
• Mentor and grow a high-performing AI team, fostering cross-functional collaboration to scale impact and accelerate AI maturity.
• Own and deliver business-aligned OKRs, ensuring AI initiatives are outcome-driven and tied to enterprise value creation.
• Independently lead and deliver large-scale AI/GenAI initiatives, ensuring end-to-end execution from design to deployment with minimal oversight
• Oversee full Model Development Life Cycle (MDLC) from data exploration and feature engineering to model training, validation, deployment, and performance monitoring.
• Collaborate with product, data, engineering, and MLOps teams to align AI initiatives with business needs and ensure scalable, production-ready deployment.
• Champion integration of AI and GenAI into operational workflows, partnering with MLOps to operationalize models using platforms like AWS SageMaker. Drive change management across business unit.
• Demonstrate strong technical skills, including Python, SQL, and working knowledge of cloud platforms (e.g., AWS), as well as UI tools such as React, Streamlit, or APIs to enable model consumption.
• Drive data and software vendor evaluations for small to mid-scale PoCs, ensuring alignment with technical requirements, integration feasibility, and business impact
• Infuse industry perspective by actively participating in relevant conferences and disseminating key insights across broader internal and external audiences.
• Travels to events and vendor meetings as needed (< 10%).

What You’ll Bring

• Degree in AI, Computer Science, Data Science, Engineering, Mathematics or a related field; advanced degree preferred.
• 10+ years of experience in AI, advanced analytics, or business intelligence roles with a strong record of delivering measurable business impact.
• 5+ years of experience managing and scaling AI teams, with a focus on driving enterprise-wide initiatives.
• 3+ years of experience delivering production grade models for core business domains. Experience with Bias testing framework is a plus.
• Familiarity with modern platforms including Snowflake/Databricks, GitHub, and Python/SQL-based ecosystems, and demonstrated ability to lead teams leveraging these tools.
• Preferred: Experience delivering real-time AI/ML solutions in the consumer finance domain, with emphasis on speed, reliability, and compliance.
• Deep expertise in supervised and unsupervised ML models (e.g., XGBoost, deep learning, k-means), including regularization (Ridge, Lasso, Elastic Net), variable selection, feature creation (e.g., binning, transformations, category reduction), and validation methods (hold-outs, CV, bootstrap).
• Executive-level communication and presentation skills, with the ability to influence and align senior stakeholders.
• Possess working knowledge and theoretical understanding of Retrieval-Augmented Generation (RAG) based GenAI architectures, including vector databases, embedding models, and orchestration layers.
• Cross-functional collaboration experience, enabling enterprise-wide analytics adoption and capability building.