Job Description

We are seeking an exceptionally innovative and strategic Product & Technology Lead to spearhead the development and deployment of next-generation Artificial Intelligence (AI) solutions within our Investment Banking division. This critical role is designed for a visionary leader who can seamlessly integrate deep product management expertise with robust technology leadership, specifically to drive transformative AI and machine learning (ML) capabilities. The ideal candidate will identify high-impact AI opportunities across Investment Banking, translate complex business challenges into cutting-edge technical solutions, and lead multi-disciplinary teams from concept to production, ensuring our AI products deliver significant competitive advantage and efficiency gains.

Responsibilities:

AI Product Strategy & Roadmap:

Define and execute the overarching AI product strategy and roadmap for Investment Banking, identifying key opportunities to leverage advanced AI/ML, Generative AI, and data science for new product creation, existing product enhancement, and process optimization.

End-to-End AI Product Development:

Lead the entire lifecycle of AI-driven products, from initial AI use case identification, ideation, and proof-of-concept to design, development, deployment, and ongoing optimization. This includes establishing clear AI model requirements, data governance, and validation frameworks.

Technology Leadership for AI Solutions:

Provide senior technical and management oversight for technology teams specializing in AI/ML model development, MLOps, data engineering, and the integration of AI components into production systems. Ensure scalable, robust, and performant AI architectures.

AI Integration & Innovation:

Drive the identification, evaluation, and strategic integration of state-of-the-art AI technologies (e.g., Large Language Models, Reinforcement Learning, advanced analytics) into Investment Banking products across deal origination, advisory, trading, risk management, client engagement, and operational efficiency.

Cross-Functional AI Collaboration:

Act as the primary liaison and technical bridge between AI research, data science, deal teams, advisory groups, and technology teams. Translate complex AI concepts into understandable business value propositions and technical requirements.

Data Strategy & Management for AI:

Oversee the data strategy crucial for AI initiatives, including data acquisition, cleansing, feature engineering, and ensuring data quality and accessibility for model training and inference.

Resource & Budget Management:

Strategically manage resources, allocate budget, and plan financial oversight for high-priority AI product and technology initiatives, ensuring alignment with overall business objectives and maximizing ROI from AI investments.

AI Risk Management & Ethical AI:

Implement and enforce rigorous risk assessment frameworks specific to AI/ML models, including model validation, bias detection, explainability, and adherence to ethical AI principles. Ensure compliance with regulatory requirements and internal policies related to AI deployment in financial services.

Market Intelligence & AI Landscape:

Continuously monitor the evolving AI landscape, conduct competitive analysis, and identify emerging AI technologies and trends relevant to Investment Banking to maintain a leading edge.

Performance Monitoring & AI Model Optimization:

Define and track key performance indicators (KPIs) for AI product success, model accuracy, and efficiency gains. Implement continuous monitoring and retraining strategies for deployed AI models.

Influence & Thought Leadership:

Serve as a thought leader in AI within Investment Banking, influencing strategic decisions, driving adoption of AI best practices, and communicating the value and impact of AI solutions to all levels of the organization.

Team Building & Mentorship:

Recruit, mentor, and lead high-performing teams of AI product managers, data scientists, and ML technology professionals, fostering a culture of innovation, experimentation, and technical excellence.

Qualifications:

Experience:

Significant experience in product management and/or technology leadership, with a significant portion dedicated to building and deploying AI/ML solutions, preferably within Investment Banking or FinTech.

AI/ML Expertise:

Deep, hands-on experience with AI/ML concepts, algorithms, and practical application. Proven track record of leading the development and deployment of complex AI models into production environments. Familiarity with MLOps principles, modern AI/ML frameworks (e.g., TensorFlow, PyTorch), and cloud-based AI platforms.

Investment Banking Acumen:

Strong understanding of Investment Banking products, deal lifecycles, advisory processes, and regulatory landscapes. Ability to identify specific pain points and opportunities where AI can deliver significant business value.

Product Leadership:

Demonstrated success in leading end-to-end product development lifecycles for complex software products, with strong skills in strategic planning, requirements definition, and go-to-market execution.

Technology Leadership:

Prior experience providing senior-level oversight or direct management of technology teams, particularly those focused on AI/ML development, data engineering, or quantitative analysis.

Data Fluency:

Strong understanding of data architectures, big data technologies, and data governance practices essential for enterprise-level AI deployments.

Analytical & Problem-Solving:

Exceptional analytical, quantitative, and problem-solving skills, with a proven ability to translate ambiguous business problems into clear, solvable AI challenges.

Leadership & Management:

Demonstrated experience managing multiple high-performing teams, influencing senior leaders, and strategic control over resources and budgets.

Communication:

Superior written and verbal communication skills, capable of articulating highly technical AI concepts and their business implications to diverse audiences, from technology professionals to executive leadership.

Risk & Compliance:

Solid understanding of risk management, model validation, and compliance requirements pertinent to AI/ML in financial services.

Education:

Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, Quantitative Finance, or a related highly analytical field. Master's degree or PhD in a relevant quantitative or AI-focused discipline preferred.

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Job Family Group:

Technology
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Job Family:

Applications Development
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Time Type:

Full time
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Most Relevant Skills

Please see the requirements listed above.
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Other Relevant Skills

For complementary skills, please see above and/or contact the recruiter.
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