Data & AI Product Manager MMH261030-2

Guardrisk; Sandton; On-site; ZAR 900,000 - 1,300,000; Full time

Job summary

Guardrisk seeks an experienced Data & AI Product Manager to own data and AI products from discovery through rollout, adoption and value realisation. You will work embedded with business teams, understand workflows, uncover underlying problems, and shape reusable, certified data products in Guardrisk’s Data CoE.

You will collaborate with data engineers, analysts and AI engineers to balance user need, technical trade-offs and speed, ensuring measurable value beyond initial deployment.

Qualifications

• Significant product-management experience with data/analytics/AI-enabled products.
• Experience taking complex products from discovery through delivery and adoption.
• Strong discovery skills: work with users to understand workflows and outcomes.
• Experience developing strategies, roadmaps, backlogs, requirements and release plans.
• Understanding of product lifecycle and post-launch management.
• Experience defining success measures using adoption and business outcomes.
• Sufficient technical fluency to discuss data engineering and AI trade-offs.
• Strong stakeholder-management and cross-functional collaboration.

Responsibilities

• Build trusted relationships with business teams to understand processes and pain points.
• Lead product and problem discovery, distinguishing requests from underlying needs.
• Translate needs into opportunities for data, analytics, ML models and AI solutions.
• Develop clear visions, strategies and value cases linked to business outcomes.
• Own a portfolio of data and AI products with coherent roadmaps and lifecycles.
• Prioritise backlogs balancing value, strategic fit, reuse and risk.
• Define requirements for data engineering, analytics and AI teams.
• Collaborate with engineers, data scientists and AI engineers on trade-offs.
• Plan releases and drive rollout, adoption and operational change.
• Define success metrics and monitor usage, feedback and value realization.
• Use live-product evidence to decide on improvements or retirement.
• Identify reusable data/AI products from one-off requests.
• Maintain clear product documentation and stakeholder alignment.
• Help establish mature data product-management disciplines.
• Ensure data products are AI-ready, documented, governed and certified.
• Coordinate with Technology and enterprise AI teams for trusted data products.

Skills

Product management

Data/Analytics/AI products

Stakeholder management

User discovery & problem framing

Roadmaps & backlog prioritisation

Communication skills

Commercial/analytical thinking

Technical fluency (data/AI)

Education

Bachelor’s degree in Business/CS/IS/Engineering/Data Analytics

Equivalent practical experience

Tools

Azure DevOps

Job description

Role Purpose

Own a portfolio of data and AI products within the Data CoE, from discovery through rollout, adoption, value realisation and continuous improvement, built on trusted, AI-ready data.

What Makes This Different

• Embedded with business teams, not waiting for requirements
• Understands how data is created, used and trusted in each business workflow
• Distinguishes stated requests from the underlying problem
• Shapes data, analytics, ML and AI opportunities on Guardrisk’s data platform
• Owns data and AI product vision, roadmap, backlog, releases and lifecycle
• Works closely enough to engineering to make credible trade-offs
• Drives data and AI products into real operational use
• Monitors usage, value and feedback after launch
• Converts local needs into reusable, certified data and AI products where appropriate

This is closer to a Forward Deployed Product Managerthan a traditional internal Product Owner. The role is embedded with stakeholders and accountable for product outcomes, deployment into production, adoption and turning operational reality into product direction.

Expected seniority:

A mature hire who has already taken complex data, analytics or AI products from problem discovery into production and adoption.

Scope boundary:

AI and automation in the Data CoE means solutions whose inputs, outputs or runtime sit on Guardrisk's data platform and data products, such as analytics, reporting, data operations, models, insight agents and natural language access to data. Automating business workflows owned by a business unit, such as claims handling, underwriting intake or finance operations, sits outside this role and is coordinated through the CDTO Front Door.

Requirements

Qualifications

• Bachelor’s degree in Business, Computer Science, Information Systems, Engineering, Data/Analytics or a related discipline, or equivalent practical experience.
• Significant product-management experience with data, analytics or AI-enabled products.
• Demonstrated experience taking complex products from problem discovery and definition through delivery and into operational adoption.
• Strong product discovery skills, including the ability to work with users and stakeholders to understand workflows, problems and desired outcomes.
• Experience developing product strategies, roadmaps, prioritised backlogs, requirements and release plans.
• Strong understanding of the product lifecycle and experience managing products beyond initial launch.
• Experience defining product success measures and using adoption, usage, customer/user feedback and business outcomes to drive product decisions.
• Sufficient technical fluency to work credibly with data engineering, architecture, data science and AI teams and understand the implications of technical trade-offs.
• Strong stakeholder-management skills and the ability to bring together people with different business and technical perspectives.
• Strong written and verbal communication skills, with the ability to make complex problems understandable and create clarity from ambiguity.
• Commercial and analytical thinking, with the ability to prioritise opportunities according to expected value rather than stakeholder volume or enthusiasm.

Guardrisk's data product model makes lifecycle management substantive rather than administrative. Data products have named consumers, dependencies, adoption metrics and a certification level (Certified, Fit for purpose or Provisional), and move through Idea ? Discovery ? Build ? Live ? Deprecated ? Retired.

Preferred Qualifications

• Experience managing data products, analytics products, ML models or AI products, particularly where the end product changes a business decision.
• Experience working in a mature digital product environment such as digital banking, fintech, telecommunications, e-commerce, SaaS or data and analytics consulting.
• Experience operating in a forward-deployed or highly business-embedded product role, working directly with users while remaining closely connected to engineering teams.
• Understanding of modern data-product concepts, including data quality, lineage, semantics, certification, APIs, analytical products and reusable data assets.
• Practical understanding of machine learning, generative AI and agentic AI, sufficient to shape AI products on data and spot where better data unlocks AI value.
• Familiarity with agile product-development practices and tools such as Azure DevOps or equivalent product-delivery platforms.
• Experience managing a portfolio of related data and AI products, not only a single application or delivery project.
• Experience establishing product adoption, usage and value measurement and using those measures to make investment, improvement or retirement decisions.
• Experience turning business-specific requirements into scalable or reusable data and AI products that can serve multiple user groups.
• Experience in financial services, insurance or another regulated environment.
• Experience working across organisational boundaries where successful delivery requires coordination between business, product, engineering, architecture and governance teams.

Duties and Responsibilities

We’re building practical Data & AI capability within Guardrisk’s Data CoE, turning real business problems into trusted data products, analytics and AI solutions that people actually use.

We’re looking for an experienced Data & AI Product Manager to own data and AI products from initial discovery through delivery, rollout, adoption, value realisation and continuous improvement.

This is not a traditional internal Product Owner role where requirements arrive ready to be put into a backlog. You will work closely with business teams, understand how decisions are made and where data is created and used today, uncover the real problems behind requests, and determine where data products, analytics or AI can make a meaningful difference.

You will then bring business and technology teams together around a clear product vision, make prioritisation and scope trade-offs, and stay accountable after launch to ensure products are adopted and deliver the intended value.

Within Guardrisk’s Data CoE, the Product Manager runs the CoE’s data door. Requests come in through the CDTO Front Door, and once they’re routed to the CoE, the Product Manager shapes, sizes, prioritises and owns them, ensuring products address business needs, support adoption and deliver measurable value.

What You Will Do

• Build trusted relationships with business teams and work alongside them to understand their processes, decisions, pain points and opportunities.
• Lead product and problem discovery, distinguishing a stakeholder’s initial request from the underlying business need.
• Translate business needs into compelling opportunities for data products, analytics, ML models and AI solutions on Guardrisk’s data platform.
• Develop clear product visions, strategies and value cases that connect investment to business outcomes.
• Own a portfolio of data and AI products and maintain coherent roadmaps across discovery, build, launch, growth, improvement and eventual retirement.
• Own and prioritise product backlogs, balancing business value, strategic fit, reuse, risk, dependencies and delivery effort.
• Define product requirements, outcomes and acceptance criteria with enough clarity for data engineering, analytics and AI teams to deliver effectively.
• Work closely with data engineers, architects, analysts, data scientists and AI engineers to make informed trade-offs between user need, technical complexity, speed and long‑term reuse.
• Plan releases and work with business teams to drive rollout, adoption and operational change, rather than treating technical deployment as the finish line.
• Define how product success will be measured and monitor usage, adoption, feedback and realised value after launch.
• Use evidence from live products to determine what should be improved, expanded, consolidated or retired.
• Identify where a requirement initially raised by one part of the business can become a reusable, certified data or AI product rather than a one‑off solution.
• Maintain clear product documentation and ensure stakeholders understand product direction, priorities, decisions and progress.
• Help establish mature data product-management disciplines across Guardrisk’s growing Data CoE.
• Make sure data products are AI-ready, meaning documented, governed, certified and fit for use by the analytics, ML and AI solutions that consume them.
• Work with Technology, and with the teams across Guardrisk that lead process automation and enterprise AI, so that trusted data products are consumed rather than rebuilt.