AI Product Strategy Manager - Credit Cards

📍 Job Overview

Job Title: AI Product Strategy Manager - Credit Cards

Company: Lloyds Banking Group

Location: Chester or Manchester, United Kingdom

Job Type: Full time

Category: Product Management / AI Strategy / Financial Services Operations

Date Posted: 2026-08-03

Experience Level: Mid-Level (3-5 years)

Remote Status: Hybrid

🚀 Role Summary

• Drive AI and Machine Learning product strategy specifically within the Credit Cards business unit.

• Lead the identification, prioritization, and execution of high-impact AI use cases to enhance customer outcomes and business growth.

• Manage the AI product backlog, fostering rapid test-and-learn cycles to validate AI solution viability and business impact.

• Collaborate closely with engineering, data science, product, and business teams to translate AI opportunities into actionable experiments and scaled solutions.

• Act as a key liaison, bridging technical AI capabilities with business needs and facilitating adoption across the organization.

📝 Enhancement Note: This role is positioned within a large, established financial institution, implying a focus on regulated environments, robust data governance, and scalable AI solutions. The "Product Strategy Manager" title, combined with "hands-on" AI experimentation, suggests a hybrid role balancing strategic vision with tactical execution in the AI/ML product lifecycle. The emphasis on Credit Cards indicates a need for domain-specific understanding of consumer lending and risk management.

📈 Primary Responsibilities

• Identify and prioritize high-value AI/ML use cases across the Credit Cards lifecycle, aligning with strategic business objectives.

• Develop and maintain a clear, actionable AI product backlog, ensuring alignment with engineering and data science roadmaps.

• Design and lead rapid experimentation and proof-of-concept initiatives to test AI hypotheses and demonstrate measurable value.

• Define key performance indicators (KPIs) and success metrics for AI initiatives, tracking progress and reporting on outcomes.

• Collaborate with cross-functional teams (Engineering, Data Science, Product, Business Units) to ensure seamless integration and successful scaling of AI solutions.

• Translate complex AI concepts and technical findings into clear, concise language for non-technical stakeholders, driving understanding and buy-in.

• Support the growth of AI capabilities within the organization through knowledge sharing, coaching, and potentially managing a small team.

• Identify and evaluate potential external partnerships to accelerate AI adoption and innovation.

• Ensure AI solutions adhere to regulatory requirements, data governance policies, and ethical AI principles within the financial services sector.

• Stay abreast of emerging AI/ML trends, tools, and techniques, assessing their applicability to the Credit Cards business.

📝 Enhancement Note: The explicit mention of "hands-on use of AI tools," "prompting, fine-tuning," and "agentic AI" suggests this role requires practical engagement with AI technologies, not just strategic oversight. The responsibility to "support capability growth of self and others through coaching and/or management of a small team" indicates potential leadership and mentorship expectations.

🎓 Skills & Qualifications

Education:

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, Statistics, Business Administration, or a related quantitative field. Experience:

• 3+ years of experience delivering AI, Machine Learning, Data Science, or advanced analytics solutions end-to-end, with clear evidence of business impact.

• 3+ years of experience shaping or delivering AI strategy, ideally within consumer lending, credit cards, financial services, or another customer-led, regulated environment. Required Skills:

• Hands-on experience using modern AI tools and techniques, including generative AI, agentic AI, machine learning, or applied AI experimentation.

• Strong experience working with cloud-based data and AI platforms, ideally including GCP, BigQuery, MLOps, CI/CD pipelines, or version control tools such as Git.

• Proven ability to translate ambiguous opportunities into prioritized backlogs, experiments, deliverables, and measurable outcomes.

• Strong stakeholder management skills, with the ability to simplify technical topics for non-technical audiences and influence across business, data, engineering, and platform teams.

• Deep understanding of the AI/ML product lifecycle, from ideation and experimentation to deployment and iteration.

• Familiarity with agile methodologies and backlog management principles. Preferred Skills:

• Experience with internal data ecosystems, data products, or large-scale customer data platforms.

• Experience with advanced GenAI use cases such as RAG (Retrieval-Augmented Generation), copilots, agentic workflows, NLP (Natural Language Processing), document processing, or conversational AI.

• Exposure to real-time decisioning, streaming architectures, model governance, or regulatory environments within financial services.

• Experience leading small teams, mentoring others, or managing cross-functional workstreams.

• Familiarity with financial services products, particularly credit cards, and their associated data and operational nuances.

📝 Enhancement Note: The requirement for "3+ years’ experience delivering AI, machine learning, data science or advanced analytics solutions end-to-end, with clear evidence of business impact" and "3+ years’ experience shaping or delivering AI strategy" points towards a mid-level role, likely requiring a blend of technical acumen and strategic foresight. The emphasis on specific cloud platforms (GCP, BigQuery) and MLOps/CI/CD/Git indicates a preference for candidates familiar with modern software development and deployment practices for AI.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrable case studies showcasing the end-to-end delivery of AI/ML solutions, highlighting problem definition, methodology, execution, and quantifiable business impact.

• Examples of AI strategy development, including use case identification, prioritization frameworks, and roadmap creation.

• Documentation of experience with AI experimentation, including experimental design, hypothesis testing, and analysis of results.

• Evidence of managing product backlogs for AI initiatives, including prioritization, user story creation, and sprint planning.

• Portfolio pieces illustrating collaboration with diverse teams (e.g., engineering, data science, business stakeholders) to drive AI adoption. Process Documentation:

• Ability to document AI/ML workflows, from data ingestion and model training to deployment and monitoring.

• Experience in defining and tracking key performance indicators (KPIs) and success metrics for AI products.

• Examples of translating complex technical AI concepts into accessible documentation for business audiences.

• Showcase of familiarity with MLOps principles and CI/CD pipelines for robust AI system deployment.

• Documentation of experience working within regulated environments, ensuring compliance and governance in AI processes.

📝 Enhancement Note: Given the role's focus on AI product strategy and hands-on experimentation within a regulated industry, a portfolio should emphasize not just technical execution but also strategic thinking, stakeholder management, and demonstrable business value. Portfolio items should clearly articulate the "why" behind AI initiatives, the "how" of their implementation, and the "what" of their impact, including any considerations for regulatory compliance.

💵 Compensation & Benefits

Salary Range: £72,702 - £80,780 per annum.

Benefits:

• A generous pension contribution of up to 15%.

• An annual performance-related bonus.

• Share schemes, including free shares.

• Benefits tailored to lifestyle, such as discounted shopping.

• 30 days of holiday, plus bank holidays.

• A range of wellbeing initiatives.

• Generous parental leave policies.

Working Hours: Full-time, with an expectation of approximately 40 hours per week. The work style is hybrid, requiring at least two days per week (or 40% of time) in an office site (Chester or Manchester).

📝 Enhancement Note: The specified salary range of £72,702 - £80,780 for Manchester/Chester aligns with mid-level to senior specialist roles in the UK's financial services and technology sectors, particularly for positions requiring specialized AI/ML expertise and strategic product management. This range is competitive for the region and reflects the seniority and critical nature of the AI Product Strategy Manager role. The inclusion of specific benefits like a generous pension, annual bonus, and share schemes is typical for large financial institutions like Lloyds Banking Group and adds significant value beyond the base salary.

🎯 Team & Company Context

🏢 Company Culture

Industry: Financial Services (specifically Retail Banking, Credit Cards). Lloyds Banking Group is one of the UK's largest financial institutions, with a long history and a significant market presence. This context implies a focus on customer-centricity, regulatory compliance, and a commitment to responsible innovation.

Company Size: Large Enterprise (over 10,000 employees, based on typical scale for Lloyds Banking Group). This size suggests a structured environment with established processes, ample resources, and opportunities for cross-departmental collaboration, but also potential for navigating complex organizational dynamics.

Founded: The Group has a history dating back to 1765, indicating a stable, established organization with a deep understanding of the financial landscape. This longevity suggests a culture that values tradition alongside a forward-looking approach, especially with its current focus on AI transformation.

Team Structure:

• The AI Product Strategy Manager will likely be part of a dedicated AI, Data Science, or Digital Transformation team within the Credit Cards division.

• This team may comprise AI/ML engineers, data scientists, product owners, and other strategy managers.

• Reporting structure will likely be to a Head of AI/Data Science or a senior Product Lead, with close collaboration across various business functions (e.g., Marketing, Risk, Operations, IT).

• Cross-functional collaboration is a core aspect, requiring strong partnerships with engineering teams responsible for cloud platforms and MLOps, as well as business stakeholders who own the Credit Cards product. Methodology:

• Data Analysis & Insights: Emphasis on data-driven decision-making, leveraging internal customer data and AI/ML models to derive actionable insights for product improvement and strategic planning.

• Workflow Planning & Optimization: Focus on streamlining processes for AI use case identification, experimentation, and deployment, aiming for efficiency and scalability within a regulated environment.

• Automation & Efficiency Practices: Utilizing AI and ML to automate tasks, enhance decision-making, and improve operational efficiency across the Credit Cards lifecycle. This includes adopting MLOps and CI/CD practices for robust and repeatable AI deployments.

Company Website: https://www.lloydsbankinggroup.com/

📝 Enhancement Note: Operating within a large, regulated financial institution like Lloyds Banking Group means that AI initiatives must be robust, secure, compliant, and scalable. The "AI Product Strategy Manager" role will need to balance innovation with risk management, demonstrating clear value and adherence to industry standards. The hybrid working model indicates a modern approach to workplace flexibility within a traditional sector.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned as a mid-level to senior specialist within the AI and Product Management domain. It requires a blend of strategic thinking, hands-on technical application, and cross-functional leadership, suitable for individuals looking to deepen their expertise in AI product development within a significant industry.

Reporting Structure: The role reports into a senior leader within the AI/Data Science or Digital Transformation function, likely a Head of AI or Director of Product. This structure offers exposure to senior leadership and strategic decision-making.

Operations Impact: The AI Product Strategy Manager will have a direct impact on the Credit Cards business by identifying and implementing AI solutions that enhance customer experience, drive revenue growth, improve operational efficiency, and manage risk. The success of AI initiatives will be measured by tangible business outcomes and the adoption of AI across the product lifecycle.

Growth Opportunities:

• AI Skill Advancement: Opportunity to work with cutting-edge AI technologies (Generative AI, Agentic AI) and cloud platforms (GCP), deepening technical expertise.

• Product Leadership: Progression to senior product management roles, leading larger product portfolios or strategic AI initiatives.

• Domain Expertise: Developing deep knowledge of the financial services and credit card industry, becoming a subject matter expert in AI applications within this sector.

• Team Leadership: Potential to lead a team of AI specialists, data scientists, or product analysts, developing management and mentorship skills.

• Cross-functional Influence: Building a strong network and reputation for driving strategic change across various departments within a large enterprise.

📝 Enhancement Note: The "AI Product Strategy Manager" role is a critical stepping stone for professionals aiming for leadership positions in AI product management, especially within regulated industries. The hybrid nature of the role and the emphasis on hands-on experimentation offer a dynamic learning environment. The potential for team leadership and the direct impact on a major business line like Credit Cards provide significant career development pathways.

🌐 Work Environment

Office Type: Hybrid working model, requiring at least two days per week at either the Chester or Manchester office. This suggests a modern office setup designed to facilitate collaboration and connection for teams working remotely part of the week.

Office Location(s): Chester and Manchester, United Kingdom. These are major commercial hubs in the North West of England, offering good connectivity and access to talent.

Workspace Context:

• Collaborative Environment: Offices are likely equipped with meeting rooms, breakout spaces, and technology to support hybrid collaboration between on-site and remote team members.

• Operations Tools & Technology: Access to modern cloud platforms (GCP), MLOps tools, CI/CD pipelines, and version control systems (Git) will be integral to the daily workflow. The company will provide necessary hardware and software.

• Team Interaction: Opportunities for regular interaction with data scientists, AI engineers, product managers, and business stakeholders through structured meetings, informal discussions, and project-specific collaborations.

Work Schedule: Standard full-time hours (approximately 40 per week), with flexibility inherent in the hybrid model allowing for a balance between office-based work and remote working. This structure supports focused work on AI experimentation and strategy development.

📝 Enhancement Note: The hybrid model is a key feature, indicating that candidates should be comfortable working both independently from home and collaboratively in an office setting. The presence of specific cloud and MLOps tools suggests a tech-forward environment within a traditional financial institution, requiring adaptability and proficiency in modern development practices.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: HR or Recruiter call to assess basic qualifications, experience, and cultural fit.

• Hiring Manager Interview: In-depth discussion about experience, responsibilities, and alignment with the AI Product Strategy Manager role. Focus on AI strategy, product lifecycle, and stakeholder management.

• Technical/Case Study Interview: Assessment of hands-on AI/ML skills, problem-solving abilities, and strategic thinking. This may involve discussing past projects, a hypothetical AI use case scenario, or a technical challenge related to AI experimentation or strategy. Candidates might be asked to present their approach to a specific credit card-related AI problem.

• Panel Interview: Interaction with cross-functional team members (e.g., Data Science Lead, Engineering Manager, Business Stakeholder) to evaluate collaboration, communication, and ability to influence.

• Final Interview: Potentially with a senior leader to discuss career aspirations, strategic vision, and overall fit within the organization.

Portfolio Review Tips:

• Highlight AI Strategy: Showcase examples of how you've identified, prioritized, and strategized AI initiatives, clearly linking them to business objectives.

• Demonstrate Hands-On Experience: Include specific projects where you actively used AI/ML tools, conducted experiments, or fine-tuned models. Detail the tools and techniques used.

• Quantify Impact: For each project, clearly state the business problem, your solution, the metrics used to measure success, and the quantifiable outcomes (e.g., improved customer retention by X%, reduced operational costs by Y%).

• Showcase Collaboration: Provide examples of how you've worked with diverse teams, translating technical concepts and influencing decision-makers.

• Address Regulatory Context: If applicable, include examples of how you've considered or implemented AI solutions within regulated environments, addressing data governance and compliance.

• Structure for Clarity: Organize your portfolio logically, perhaps by use case or by stage of the AI product lifecycle, with concise summaries for each item.

Challenge Preparation:

• AI Use Case Ideation: Be prepared to brainstorm AI use cases for credit cards (e.g., fraud detection, customer segmentation, personalized offers, risk assessment, customer service automation).

• Experiment Design: Understand how to design effective A/B tests or experiments to validate AI hypotheses, including defining control groups, metrics, and potential pitfalls.

• Stakeholder Communication: Practice explaining complex AI concepts and the value proposition of AI solutions to non-technical audiences clearly and persuasively.

• Cloud & MLOps Knowledge: Brush up on your understanding of cloud AI platforms (GCP), MLOps principles, and CI/CD for AI deployment.

• Regulatory Awareness: Familiarize yourself with general AI ethics and data privacy considerations relevant to financial services.

📝 Enhancement Note: The interview process will likely assess both strategic vision and practical execution capabilities. Candidates should be prepared to discuss their experience with the full AI product lifecycle, from ideation and strategy to hands-on experimentation and scaling. A strong portfolio that clearly demonstrates tangible business impact and cross-functional collaboration will be crucial.

🛠 Tools & Technology Stack

Primary Tools:

• AI/ML Experimentation Platforms: Experience with tools for model building, training, and experimentation. This could include libraries like TensorFlow, PyTorch, scikit-learn, or managed services on cloud platforms.

• Generative AI Tools: Familiarity with LLM APIs (e.g., OpenAI, Google AI), prompt engineering techniques, and potentially fine-tuning frameworks.

• Cloud-based Data & AI Platforms: Strong experience with Google Cloud Platform (GCP), including services like BigQuery for data warehousing, Vertex AI for ML model development and deployment, and other relevant GCP AI/ML services.

• Version Control: Proficient use of Git for code management and collaboration.

Analytics & Reporting:

• Data Warehousing/Lakes: Experience with BigQuery or similar cloud data warehousing solutions for data analysis and preparation.

• BI Tools: Familiarity with business intelligence tools (e.g., Tableau, Looker, Power BI) for data visualization and reporting on AI initiative performance.

• Experimentation Tracking: Tools or methods for tracking and analyzing results from A/B tests and AI experiments.

CRM & Automation:

• CRM Systems: Understanding of how AI can integrate with or enhance CRM functionalities for customer insights and personalized experiences (though not explicitly required, it's relevant context).

• MLOps & CI/CD Tools: Experience with MLOps practices and tools for automating the AI model lifecycle (e.g., Kubeflow, MLflow) and CI/CD pipelines (e.g., Jenkins, GitLab CI, Cloud Build) for seamless deployment.

• Integration Tools: Understanding of how to integrate AI models and services into existing business workflows and applications.

📝 Enhancement Note: The specific mention of GCP, BigQuery, and MLOps/CI/CD pipelines indicates a preference for candidates who are not only AI strategists but also comfortable with modern cloud-native development and deployment practices for machine learning models. Proficiency in these areas will be a significant advantage.

👥 Team Culture & Values

Operations Values:

• Customer Centricity: A core value in financial services, focused on improving customer outcomes through AI-driven solutions. This means AI initiatives should demonstrably benefit customers.

• Data-Driven Decision Making: Emphasizing the use of data and AI/ML to inform strategic choices, prioritize initiatives, and measure impact rigorously.

• Innovation & Experimentation: Encouraging a culture of trying new approaches, rapid testing, and learning from both successes and failures in the AI space.

• Integrity & Compliance: Adhering to strict regulatory standards, ethical AI principles, and robust data governance policies, especially critical in financial services.

• Collaboration & Teamwork: Fostering an environment where diverse teams work together effectively to bring AI solutions to life, breaking down silos between technical and business functions.

• Efficiency & Scalability: Aiming to build AI solutions that are not only effective but also scalable and contribute to operational efficiency across the organization.

Collaboration Style:

• Cross-Functional Integration: Expect a collaborative style that bridges the gap between AI/Data Science teams and business units like Credit Cards, Marketing, Risk, and IT.

• Process Review & Feedback: An open culture for reviewing AI development processes, sharing feedback, and continuously improving methodologies.

• Knowledge Sharing: Encouraging the sharing of insights, best practices, and learnings from AI experiments and projects across teams and departments.

• Agile & Iterative: Working in agile sprints, with a focus on iterative development, continuous integration, and regular communication to adapt to evolving requirements and findings.

📝 Enhancement Note: The emphasis on "AI transformation" within a large, established financial group suggests a culture that is evolving, seeking to balance innovation with its inherent responsibilities. Candidates should demonstrate an understanding of how to drive change within such an organization, highlighting collaboration, data-driven insights, and a commitment to ethical and compliant AI practices.

⚡ Challenges & Growth Opportunities

Challenges:

• Navigating a Regulated Environment: Balancing rapid AI innovation with stringent financial services regulations, data privacy laws, and model governance requirements.

• Data Complexity & Accessibility: Working with large, complex, and potentially siloed datasets within a legacy organization, requiring effective data wrangling and access strategies.

• Cultural Change Management: Driving adoption of AI and new ways of working across a large, established organization, requiring strong communication and stakeholder management skills.

• Proving ROI for AI: Clearly demonstrating the tangible business value and return on investment for AI initiatives, especially in areas where impact might be indirect or long-term.

• Keeping Pace with AI Advancements: Continuously learning and adapting to the rapidly evolving landscape of AI technologies and techniques to maintain a competitive edge.

Learning & Development Opportunities:

• AI Skill Advancement: Deepen expertise in areas like Generative AI, Agentic AI, MLOps, and advanced ML techniques through hands-on work and potential training.

• Industry Conferences & Certifications: Opportunities to attend relevant AI, data science, and product management conferences, and pursue certifications in cloud platforms or AI technologies.

• Mentorship & Leadership Development: Potential for mentorship from senior leaders and opportunities to develop leadership skills through team management or leading strategic workstreams.

• Cross-Functional Exposure: Gaining broad exposure to different business functions within Lloyds Banking Group, understanding their challenges and how AI can solve them.

• Product Strategy Refinement: Developing sophisticated product strategy skills within the high-stakes financial services sector.

📝 Enhancement Note: The challenges presented are typical for AI roles in large, regulated industries. The growth opportunities highlight the potential for significant professional development, particularly for those looking to specialize in AI product management within finance. Candidates should be prepared to discuss how they would approach these challenges and leverage the growth opportunities.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you identified a high-impact AI use case. How did you prioritize it, and what was the outcome?" (Focus on strategic thinking, prioritization frameworks, and impact measurement.)

• "How would you approach building an AI product strategy for credit card fraud detection or personalized customer offers?" (Assess your understanding of credit card business challenges and AI application.)

• "Explain a complex AI concept (e.g., RAG, MLOps) to a non-technical business leader. What are the key takeaways they should understand?" (Evaluate your communication and stakeholder management skills.) Company & Culture Questions:

• "What do you know about Lloyds Banking Group and our approach to AI and digital transformation?" (Demonstrate your research and understanding of the company's context.)

• "How do you ensure AI initiatives are compliant and ethical, especially within a regulated industry like financial services?" (Assess your awareness of regulatory and ethical considerations.)

• "Describe your experience working in a hybrid environment. How do you ensure effective collaboration with remote and in-office colleagues?" (Evaluate your adaptability to the work arrangement.) Portfolio Presentation Strategy:

• The STAR Method for Case Studies: Structure your portfolio presentations using the Situation, Task, Action, Result framework to clearly articulate your contributions and their impact.

• Visualizing Data and Impact: Use clear charts and graphs to present metrics and demonstrate the ROI of your AI initiatives. Avoid overly technical jargon in visualizations.

• Focus on Business Value: Always tie your technical work back to the business problem it solved and the value it delivered. Quantify impact wherever possible.

• Highlight Collaboration: Be ready to discuss how you collaborated with different teams, managed stakeholder expectations, and navigated challenges.

• Prepare for Q&A: Anticipate questions about your technical approach, strategic decisions, and how you would handle specific scenarios relevant to the role.

📝 Enhancement Note: Interview preparation should focus on demonstrating a blend of strategic vision, hands-on AI/ML expertise, strong communication skills, and an understanding of the financial services context. Be ready to discuss specific examples from your experience that align with the role's requirements.

📌 Application Steps

To apply for this AI Product Strategy Manager position:

• Submit your application through the provided Workday link: https://lbg.wd3.myworkdayjobs.com/LBG_Careers/job/Manchester/AI-Strategy-Manager----Credit-Cards_158191-1

• Customize your Resume: Tailor your CV to highlight your experience in AI strategy, machine learning, product management, and any relevant financial services or credit card industry exposure. Use keywords from the job description.

• Prepare Your Portfolio: Select 2-3 key projects that best showcase your ability to deliver AI solutions end-to-end, manage product strategy, and drive business impact. Be ready to present these with a focus on quantifiable results and collaboration.

• Research Lloyds Banking Group: Understand their business objectives, their stance on AI and digital transformation, and their commitment to customer outcomes and responsible innovation.

• Practice Interview Responses: Prepare for common interview questions, particularly those focusing on AI strategy, hands-on technical experience, stakeholder management, and your approach to working in a hybrid, regulated environment.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.