AI Product Manager Role

Tiger Analytics is a global AI and analytics consulting firm. With data and technology at the core of our solutions, our 4000+ tribe is solving problems that eventually impact the lives of millions globally. Our culture is modeled around expertise and respect with a team first mindset. Headquartered in Silicon Valley, you’ll find our delivery centers across the globe and offices in multiple cities across India, the US, UK, Canada, and Singapore, including a substantial remote global workforce.

We’re Great Place to Work-Certified™. Working at Tiger Analytics, you’ll be at the heart of an AI revolution. You’ll work with teams that push the boundaries of what is possible and build solutions that energize and inspire.

We at Tiger Analytics are looking for AI Product Managers in the Banking & Financial Services space. This is a new and evolving role, and while the job description (JD) below serves as a guideline, the role will evolve and grow with time, similar to the AI space itself. Candidates may come from an Analytics, Data Science, or AI consulting background, or may have extensive experience in the functional aspects of banking (commercial, retail, fraud, credit, etc.) with recent exposure to and learning in the AI space. Any sort of hands-on Analytics and Data Science experience in past or present roles is preferred.

Role Overview

The core mission is to identify and solve high-value problems for banking clients using AI. This involves managing the entire product lifecycle from ideation and client-specific customization through to deployment and delivery handoff..

Key Responsibilities

• Customization and Solutioning: Customize existing organizational AI product assets and solutions (e.g., fraud engines, NLP-based chat solutions) for specific banking client use cases, accounting for their unique data structures and regulatory environments.
• Domain Expertise & Innovation: Brainstorm and conceptualize new Banking-specific AI capabilities and solutions by leveraging your expertise as a Banking product/domain Subject Matter Expert (SME), translating emerging trends into concrete product opportunities (e.g., new generative AI applications for compliance).
• Product Definition & Prototyping: Define the product vision and features, translating client requirements into clear, measurable user stories. Lead the process of MVP (Minimum Viable Product) and Wireframe development to visually communicate proposed solutions to both internal teams and external clients.
• Execution & Delivery: Own the end-to-end product lifecycle in a client delivery context. Drive Banking AI capability delivery for both new and existing clients, coordinating with Data Science, Engineering, and Implementation teams to ensure timely and high-quality deployment.
• Technical Showcase & Enablement: Jointly work with the Technology team to build and refine AI Demos and Showcases specific to Banking and Financial Services to support sales efforts, thought leadership, and client engagement.

Primary Skillsets

1. Product Management Core:

   Product Strategy & Road mapping, GTM & Pricing, MVP definition, Agile/Scrum, Stakeholder Management, and strong analytical skills for defining and tracking KPIs.

2. Financial Services Domain:

   In-depth knowledge of banking value chains (e.g., Retail, Commercial, Investment Banking) and core financial processes (e.g., Fraud/Risk Management, Regulatory Compliance, Trading).

3. AI/ML Acumen & Prototyping

   : Understanding of the AI/ML lifecycle and core concepts including LLMs, RAG, Agentic AI. Ability to quickly develop product mockups and wireframes to articulate the AI solution.

4. Consulting & Client Focus:

   Proven ability to gather requirements from clients, customize solutions for a specific customer, and clearly articulate technical value to a non-technical audience.

5. Regulation & Ethics:

   Deep understanding of regulatory compliance in finance (e.g., KYC, AML, GDPR) and principles of Responsible AI (Fairness, Explainability, Transparency).

Secondary Skillsets

1. Technical Tools:

   Familiarity with cloud platforms (AWS, Azure, GCP) and their ML services, and experience with data visualization tools (e.g., Tableau, Power BI).

2. Solution Architecture: Basic understanding of data pipelines, integration methods (API design), and how AI models are deployed and monitored in a production banking environment.

3. Business Case Development:

   Experience in quantifying the ROI of AI projects and creating compelling business cases to gain client and executive buy-in.

4. Pre-Sales Support:

   Experience creating and conducting technical demonstrations and showcases for potential and existing clients.

5. Data & Quality Assurance: Hands-on comfort with defining data requirements for modeling and working with QA/Testing teams to validate AI model behavior and output quality.

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