HSBC
Website:
hsbc.com
Company:
https://www.linkedin.com/company/hsbc
Seniority: Not Applicable
Industries: Financial Services
Job details:
Job title: Associate Director, DAAI
- We are seeking an experienced AI product owner to lead the strategy, development and optimization of a hyper personalization and lead recommendation platform. This role bridges business, data science, engineering and growth teams to deliver intelligent recommendation systems that improve customer engagement, retention and revenue outcomes.
- The incumbent would be expected to innovate products/ solutions, define product strategies to ensure the product stays relevant in the ever-changing dynamics. This role holder is expected to driver Agentic AI solutions leveraging varied AIML technologies including ML/DL, LLM, RAG, Graph Neural Net to build the most sophisticated Recommendation Engines. The Role holder will also be expected to drive the experience of the product which includes seamless orchestration from requirements to code to data and models. The role holder will work in IWPB Chief Data & Analytics office to drive Hyper Personalised experience across app, email, web, ads and CRM Channels and act as a definitive authority on recommendation logic ensuring that personalisation user experience are both high performing and ethically sound
In This Role, You Will
Product Management
- Own the AI product vision, strategy, and roadmap aligned to business outcomes and customer needs.
- Translate business problems into clear product requirements, user stories, and measurable success metrics (OKRs/KPIs).
- Partner with stakeholders across Technology, Data, Risk, Compliance, Operations, and Front Office to prioritise use cases and manage trade-offs.
- Drive delivery through the full product lifecycle: discovery, MVP, launch, adoption, iteration, and scale.
- Define operating model for the product (support, monitoring, incident management, model refresh cadence, and change control).
Product Innovation
- Identify and validate new AI opportunities (e.g., GenAI, predictive analytics, optimisation, NLP) through structured discovery and rapid prototyping.
- Maintain awareness of market and technology trends; assess feasibility, value, and risk for adoption in an enterprise environment.
- Build business cases for AI initiatives, including benefits, costs, dependencies, and delivery approach.
- Create reusable assets (patterns, accelerators, reference architectures) to reduce time-to-value across teams.
- Champion experimentation culture while keeping focus on measurable outcomes and delivery pace.
Responsible AI
- Ensure AI solutions meet Responsible AI principles: fairness, explainability, transparency, privacy, security, and accountability.
- Partner with Model Risk Management, Compliance, Legal, and Information Security to ensure appropriate governance, documentation, and approvals.
- Define and implement controls for model performance, drift, bias monitoring, and auditability.
- Establish clear human-in-the-loop processes where required, including escalation paths and decision accountability.
- Promote responsible data usage, including consent, minimisation, retention, and lineage.
Hands-On Experimentation
- Lead hands-on development of prototypes and MVPs using modern ML/GenAI techniques and tooling.
- Design experiments, evaluation frameworks, and offline/online testing to prove value and manage risk.
- Guide feature engineering, model selection, prompt design, fine-tuning/RAG approaches, and performance optimization.
- Collaborate with engineers to productionise solutions (CI/CD, MLOps, monitoring, scalability, reliability).
- Review code, mentor team members, and set engineering standards for quality and reproducibility.
To Be Successful You Will
Core Product Skills
- Proven experience owning and scaling data/AI products end-to-end (discovery to production).
- Strong product thinking: customer-centricity, prioritization, roadmap management, and outcome-based delivery.
- Ability to communicate clearly with senior stakeholders and translate between business and technical teams.
- Experience working in Agile product delivery environments (Scrum/Kanban) with strong execution discipline.
- Excellent data storyteller & proven skills in building powerful dashboards & presentation.
AI/ML & Engineering Skills
- Strong applied experience in ML/AI (supervised/unsupervised learning, NLP, time series, optimization) and/or GenAI (RAG, prompt engineering, evaluation).
- Solid software engineering fundamentals (Python preferred), APIs, data pipelines, and production-grade practices.
- Experience with MLOps: model deployment, monitoring, drift detection, retraining strategies, and CI/CD.
- Familiarity with cloud-based AI platforms and data ecosystems (e.g., Azure/AWS/GCP), containerization, and orchestration.
- Deep understanding of Deep Learning, NLP, and Reinforcement Learning; expert-level Python and ML framework proficiency (PyTorch/TensorFlow).
- Agentic Frameworks: Hands-on experience with LangChain, LangGraph, or CrewAI for building multi-agent workflows.
- Strong Knowledge of ML concepts, Recommendation system, CDP (customer data platform), with ability to review system designs and API documentation.
- Hands on understanding of LLM, agentic reasoning loops and memory management in AI system.
- Deep understanding of Graph Theory, proficiency in graph modelling and query languages using databases.
- Quick learner and adaptable to the technological changes to stay ahead
Responsible AI / Risk & Controls
- Demonstrable experience implementing governance, model documentation, and controls in regulated environments.
- Understanding of privacy, security, and model risk considerations (bias, explainability, audit trails, data lineage).
- Ability to design evaluation and monitoring approaches that stand up to scrutiny from risk and control partners.
Leadership & Ways of Working
- Experience leading cross-functional teams (data scientists, engineers, analysts, UX, risk/control partners).
- Pragmatic, delivery-focused mindset-balances innovation with operational resilience and compliance.
Qualification
- Masters’s or PHD in Computer Science, AI or Data Science or equivalent high level industry experience.
- 10+years of experience in software engineering / Analytics, with at least 5 years specifically leading AI/ML product initiatives., with significant experience in AI Product Ownership and building systems from the ground up.
Hsbc.Com/Careers
You’ll achieve more at HSBC
HSBC is an equal opportunity employer committed to building a culture where all employees are valued, respected and opinions count. We take pride in providing a workplace that fosters continuous professional development, flexible working and, opportunities to grow within an inclusive and diverse environment. We encourage applications from all suitably qualified persons irrespective of, but not limited to, their gender or genetic information, sexual orientation, ethnicity, religion, social status, medical care leave requirements, political affiliation, people with disabilities, color, national origin, veteran status, etc., We consider all applications based on merit and suitability to the role.”
Personal data held by the Bank relating to employment applications will be used in accordance with our Privacy Statement, which is available on our website.
- Issued By HSBC Electronic Data Processing (India) Private LTD***
Click on Apply to know more.
