Product Owner
Absa
n/a - n/a
R700–750 per hour
Johannesburg, City of Johannesburg Metropolitan Municipality
Job Function
Role
Position Overview
The Product Owner – Data Science is responsible for defining, prioritizing, and delivering the strategic roadmap for data science products and services—spanning use case discovery, model development, experimentation, deployment, and ongoing value realization. The role bridges business stakeholders, data scientists, ML engineers, analytics engineers, and IT leadership to ensure models and advanced analytics solutions are business-relevant, explainable, scalable, ethically sound, and continuously optimized. The PO drives clarity, alignment, and execution across squads while promoting common standards, governance, and measurable outcomes.
Key Responsibilities
Product Vision & Strategy
-Develop and maintain a clear and practical product vision and roadmap for data science use cases that drive recommendation engines for our customers (e.g., propensity, churn, pricing, risk, personalization).
-Translate business objectives into prioritized problem statements, outcomes, hypotheses, features, and user stories with clear success metrics (e.g., uplift, ROI, value creation).
- Facilitate use case planning and centralization, aligning individual efforts into a cohesive portfolio to avoid duplication and maximize reuse (e.g., feature stores, shared components).
- Work with the Head of Data Science to define the experimentation strategies that can be explained to and tested by business units, and then track their success.
- Ensure alignment with data governance, model risk, security, and compliance requirements while balancing feasibility, cost, and time-to-value.
- Proactively surface new use cases, scale proven models, and extend value to new business lines.
Backlog Ownership & Prioritization
- Shape, own and manage the product backlog, ensuring clarity, prioritization, story mapping when needed, and value-driven delivery.
- Write and refine epics and user stories with unambiguous acceptance criteria; ensure delivered work meets quality, performance, and explainability standards.
- Maintain and socialize an integrated roadmap with specific deliverables and deadlines, updating artefacts used to inform business (e.g., exec-ready packs, progress dashboards, risks & decisions logs).
- Proactively chase and unblock outstanding requirements and dependencies; resolve misalignment between technical teams and business via granular expectation management.
Cross-Team Leadership & Collaboration
- Work with other Product Owners to coordinate teams, facilitating ceremonies, alignment sessions, and dependency management.
- Connect people and efforts across teams to leverage collective knowledge, reduce rework, and accelerate delivery.
Governance, Standards & Quality
- Partner with data governance, model risk, architecture, and security to ensure compliance with policies (privacy, bias, explainability, model approval, auditability).
- Ensure explainability and demystification of models for non-technical audiences, including clear descriptions of features, drivers, and limitations.
Stakeholder Engagement
- Serve as the primary point of contact for stakeholders regarding DS capabilities, timelines, and priorities; routinely “hold the same conversation” across audiences.
- Act as the ambassador for the team’s models—explaining what they do, how they’re validated, and how results should be interpreted and acted upon.
- Communicate experimentation constructs in business terms (e.g., A/B testing, lead splits, success metrics, guardrails), building understanding and trust.
- Continuously engage existing and new stakeholders to identify opportunities for commercialization and expansion (new clients, new features, enhanced service offerings).
- Check in with internal clients on adoption, usage patterns, and realized benefits; coordinate enhancements based on feedback.
Delivery & Performance Management
- Monitor team performance and throughput, remove blockers, and ensure timely delivery of milestones from discovery to deployment and scale.
- Drive model launch readiness (data readiness, integration points, enablement materials, runbooks) and ensure post-launch ownership is clear.
- Routinely track performance—identify peaks/dips, trigger investigations, and coordinate remediation (e.g., model recalibration, retraining, feature updates, or deprecating assets no longer in use).
- Review available dashboards and tracking for adoption, business impact, and operational health; conduct regular check-ins with internal clients on value realization and satisfaction.
Required Skills & Experience
Technical Skills
- Strong understanding of data science and ML concepts (supervised/unsupervised learning, feature engineering, model validation, A/B testing, drift monitoring, explainability).
- Ability to interpret experimentation designs, model metrics, and solution architectures; comfortable reading data/model diagrams and integration patterns.
- Working knowledge of BI and analytics tooling (e.g., Power BI dashboards for model performance and value tracking).
Product & Leadership Skills
- Proven experience as a Product Owner/Manager in a data science, ML, or advanced analytics environment.
- Demonstrated ability to lead agile teams and manage complex dependencies.
- Strong prioritization, decision-making, and negotiation skills with a focus on business outcomes and time-to-value.
- Excellent communication and storytelling—translating complex technical concepts into clear, actionable business language.
Collaboration & Governance
- Experience establishing cross-team rules, standards, and frameworks (templates, checklists, operating rhythms).
- Strong understanding of data governance, model risk management, explainability, and compliance requirements (privacy, ethics, security).
- Ability to align diverse stakeholders and teams toward shared outcomes; adept at conflict resolution and expectation management.
Qualifications
- Bachelors in Information Technology, Data Science, Computer Science, Engineering, Statistics, or related field – or equivalent working experience.
- Certification in Agile methodologies (e.g., CSPO, SAFe PO/PM) preferred.
- 2+ years of experience in data science/analytics/enterprise data environments.
- 5+ years of experience in product ownership or leadership roles delivering data or ML solutions.
Key Competencies
- Strategic thinking and long-term planning
- Strong analytical and problem-solving ability
- Leadership, facilitation, and team empowerment
- Stakeholder management and executive communication
- Experimentation mindset and outcome orientation
- Adaptability in fast-paced environments
- High attention to detail and quality
- Customer value focus and commercialization acumen
The Product Owner – Data Science is responsible for defining, prioritizing, and delivering the strategic roadmap for data science products and services—spanning use case discovery, model development, experimentation, deployment, and ongoing value realization. The role bridges business stakeholders, data scientists, ML engineers, analytics engineers, and IT leadership to ensure models and advanced analytics solutions are business-relevant, explainable, scalable, ethically sound, and continuously optimized. The PO drives clarity, alignment, and execution across squads while promoting common standards, governance, and measurable outcomes.
Key Responsibilities
Product Vision & Strategy
-Develop and maintain a clear and practical product vision and roadmap for data science use cases that drive recommendation engines for our customers (e.g., propensity, churn, pricing, risk, personalization).
-Translate business objectives into prioritized problem statements, outcomes, hypotheses, features, and user stories with clear success metrics (e.g., uplift, ROI, value creation).
- Facilitate use case planning and centralization, aligning individual efforts into a cohesive portfolio to avoid duplication and maximize reuse (e.g., feature stores, shared components).
- Work with the Head of Data Science to define the experimentation strategies that can be explained to and tested by business units, and then track their success.
- Ensure alignment with data governance, model risk, security, and compliance requirements while balancing feasibility, cost, and time-to-value.
- Proactively surface new use cases, scale proven models, and extend value to new business lines.
Backlog Ownership & Prioritization
- Shape, own and manage the product backlog, ensuring clarity, prioritization, story mapping when needed, and value-driven delivery.
- Write and refine epics and user stories with unambiguous acceptance criteria; ensure delivered work meets quality, performance, and explainability standards.
- Maintain and socialize an integrated roadmap with specific deliverables and deadlines, updating artefacts used to inform business (e.g., exec-ready packs, progress dashboards, risks & decisions logs).
- Proactively chase and unblock outstanding requirements and dependencies; resolve misalignment between technical teams and business via granular expectation management.
Cross-Team Leadership & Collaboration
- Work with other Product Owners to coordinate teams, facilitating ceremonies, alignment sessions, and dependency management.
- Connect people and efforts across teams to leverage collective knowledge, reduce rework, and accelerate delivery.
Governance, Standards & Quality
- Partner with data governance, model risk, architecture, and security to ensure compliance with policies (privacy, bias, explainability, model approval, auditability).
- Ensure explainability and demystification of models for non-technical audiences, including clear descriptions of features, drivers, and limitations.
Stakeholder Engagement
- Serve as the primary point of contact for stakeholders regarding DS capabilities, timelines, and priorities; routinely “hold the same conversation” across audiences.
- Act as the ambassador for the team’s models—explaining what they do, how they’re validated, and how results should be interpreted and acted upon.
- Communicate experimentation constructs in business terms (e.g., A/B testing, lead splits, success metrics, guardrails), building understanding and trust.
- Continuously engage existing and new stakeholders to identify opportunities for commercialization and expansion (new clients, new features, enhanced service offerings).
- Check in with internal clients on adoption, usage patterns, and realized benefits; coordinate enhancements based on feedback.
Delivery & Performance Management
- Monitor team performance and throughput, remove blockers, and ensure timely delivery of milestones from discovery to deployment and scale.
- Drive model launch readiness (data readiness, integration points, enablement materials, runbooks) and ensure post-launch ownership is clear.
- Routinely track performance—identify peaks/dips, trigger investigations, and coordinate remediation (e.g., model recalibration, retraining, feature updates, or deprecating assets no longer in use).
- Review available dashboards and tracking for adoption, business impact, and operational health; conduct regular check-ins with internal clients on value realization and satisfaction.
Required Skills & Experience
Technical Skills
- Strong understanding of data science and ML concepts (supervised/unsupervised learning, feature engineering, model validation, A/B testing, drift monitoring, explainability).
- Ability to interpret experimentation designs, model metrics, and solution architectures; comfortable reading data/model diagrams and integration patterns.
- Working knowledge of BI and analytics tooling (e.g., Power BI dashboards for model performance and value tracking).
Product & Leadership Skills
- Proven experience as a Product Owner/Manager in a data science, ML, or advanced analytics environment.
- Demonstrated ability to lead agile teams and manage complex dependencies.
- Strong prioritization, decision-making, and negotiation skills with a focus on business outcomes and time-to-value.
- Excellent communication and storytelling—translating complex technical concepts into clear, actionable business language.
Collaboration & Governance
- Experience establishing cross-team rules, standards, and frameworks (templates, checklists, operating rhythms).
- Strong understanding of data governance, model risk management, explainability, and compliance requirements (privacy, ethics, security).
- Ability to align diverse stakeholders and teams toward shared outcomes; adept at conflict resolution and expectation management.
Qualifications
- Bachelors in Information Technology, Data Science, Computer Science, Engineering, Statistics, or related field – or equivalent working experience.
- Certification in Agile methodologies (e.g., CSPO, SAFe PO/PM) preferred.
- 2+ years of experience in data science/analytics/enterprise data environments.
- 5+ years of experience in product ownership or leadership roles delivering data or ML solutions.
Key Competencies
- Strategic thinking and long-term planning
- Strong analytical and problem-solving ability
- Leadership, facilitation, and team empowerment
- Stakeholder management and executive communication
- Experimentation mindset and outcome orientation
- Adaptability in fast-paced environments
- High attention to detail and quality
- Customer value focus and commercialization acumen
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