Provide strategic and technical leadership across multiple AI/ML modeling workstreams, ensuring alignment with business
Oversee full model lifecycle across all workstreams — including development, validation, independent assessment, performance optimization, monitoring, documentation, and governance.
Establish and enforce consistent modeling standards, methodology frameworks, explainability practices, and bias testing protocols across team.
Act as a primary escalation point for model risk, governance issues, technical challenges, partnering with risk, compliance as needed.
Lead model review and approval processes, ensuring all models meet internal governance requirements and applicable regulatory standards.
Translate complex modeling outcomes and technical findings into clear, actionable insights for executive and non-technical stakeholders.
Drive evaluation and adoption of emerging AI/ML tools, platforms, and methodologies, providing evidence-based recommendations to leadership.
Recruit, mentor, and develop a high-performing team of senior and mid-level modelers, fostering a culture of technical rigor and continuous improvement.
Define team's long-term analytical roadmap, balancing innovation with BAU delivery.
Collaborate cross-functionally with data engineering, technology, operations, and business teams to align modeling solutions with broader organizational objectives.

10+ years of progressive experience in quantitative modeling, data science, or applied AI/ML, with at least 3 years in a team leadership or senior technical lead capacity.
Broad expertise across multiple modeling domains, such as predictive analytics, NLP, optimization, or AI platform evaluation.
Strong proficiency in Python; familiarity with modern AI/ML frameworks, MLOps tooling, and large-scale data platforms.
Deep understanding of end-to-end model lifecycle, including model risk management, validation frameworks, and regulatory expectations.
Proven ability to lead and develop cross-functional modeling teams in a fast-paced, delivery-oriented environment.
Experience engaging with model risk, audit, compliance, or regulatory stakeholders, and navigating governance and approval processes.
Exceptional communication skills & ability to present complex technical concepts clearly to stakeholders.
Track record of driving adoption of emerging AI/ML technologies in a structured manner.
Advanced degree (Master’s or PhD) in Statistics, Computer Science, Mathematics, Data Science, or a related quantitative discipline preferred.

AI & ML Development Lead

Key skills