Onsite
[What the role is]
[What the role is][What you will be working on]
[What you will be working on]
As a Data and AI Product Manager, your key responsibilities include:
1. Data Products Roadmap Development, Planning and Execution:
a) Develop and execute the data product roadmap, defining data products required by STB and industry stakeholders, and aligned with STB's organisational goals.
b) Create and maintain comprehensive data products requirement specifications, acceptance criteria and test cases.
c) Develop mock and/or POC dashboards and datasets, to support requirements specifications and acceptance criteria.
d) Work with Stan product team to plan and implement the data products onto Stan.
e) Support testing and production verification as part of productisation process.
f) Lead product launches and go-to-market plans for major releases of data products.
g) Drive innovation in data products, constantly seeking ways to enhance value for stakeholders.
h) Proactively identify opportunities for new data products or features based on industry trends and stakeholder needs.
2. AI Roadmap, Use Cases and Proof-of-Concept Development
a) Ideate and develop the AI roadmap for STB's data platforms, identifying high-value AI use cases that enhance data accessibility and user productivity for internal and industry stakeholders.
b) Prioritise AI roadmap items using sound evaluation frameworks that weigh feasibility, impact, and implementation readiness.
c) Lead hands-on development of AI proof-of-concepts, starting with natural language query (NLQ) for conversational access to STB's datasets and analytics.
d) Design evaluation frameworks, guardrails, and test cases for AI features, ensuring reliability, accuracy, and appropriateness before productisation.
e) Identify and shape AI use cases in close collaboration with line units, translating domain needs into technically grounded AI applications.
f) Work closely with the product implementation team to ensure AI PoCs are documented and specified to a standard that enables direct handover for productisation.
g) Stay current on applied AI developments relevant to data platforms, analytics, and public sector use cases.
3. Requirements Gathering and Product Design:
a) Conduct deep dives into business scenarios, use cases, user journey mapping as part of data and AI product solution design.
b) Translate complex stakeholder needs into clear, actionable product requirements and specifications.
c) Collaborate with Data Partnerships and Stan product team to ensure product feasibility and technical alignment.
d) As part of data and AI product design and specifications, review the end-to-end processes involved in implementing and maintaining the data products, ensure processes are streamlined and efficient.
e) Leverage digital and automation tools where possible as part of the end-to-end process design i.e. WOG tools like FormSG and Plumber to automate data collection and simple data validation checks.
4. Data Governance and Quality Management for Industry-facing data domains:
a) Ensure adherence to data governance policies for data integrity, security, and compliance.
b) Support data quality management processes, ensuring the accuracy and reliability of data products as part of productisation process.
c) Apply appropriate guardrails and evaluation criteria to AI features to ensure outputs meet governance and quality standards.
5. Stakeholder Management and Communication:
a) Build and maintain strong relationships with stakeholders across STB line units, government agencies, and industry partners, with varying levels of data and tech maturity.
b)Translate complex data and AI concepts into clear, structured communications for non-technical stakeholders, facilitating understanding and buy-in.
c) Drive alignment and clarity across stakeholders, ensuring well-defined problem statements, expectations, and outcomes.
d) Collaborate cross-functionally to ensure alignment between product development, business needs, and technical capabilities.
e) Facilitate structured discussions and decision-making, especially in ambiguous or exploratory problem spaces.
6. Analytics, Experimentation and Continuous Improvement:
a) Support data pilots in identifying and solving complex data-related problems, applying critical thinking and analytical skills.
b) Support data pilots in advanced analytics, data integration and data wrangling needs.
c) Define and track key performance indicators (KPIs) for data products implemented.
d) Analyze usage pattern, stakeholder feedback and performance metrics to inform continuous improvement.
[What we are looking for]
[What we are looking for]
The ideal candidate is formally trained in Business, Data Science, Information Systems, Computer Science, Engineering, or a related field, with at least 3–5 years of experience in data product management, data engineering, or related roles with a proven track record of delivering data-driven solutions.
Essential Skills and Knowledge
-Strong critical thinking and problem-solving skills, with the ability to break down ambiguous problems into clear, actionable components, and navigate uncertainty with confidence.
-Experience evaluating pilot projects and experimental initiatives for scalability and productisation potential.
-Familiarity with data governance frameworks, data quality management, and regulatory compliance requirements.
Technical Skills
-Strong data analytics capabilities, including data exploration, transformation, and dashboard development, with proficiency in tools such as AWS QuickSight.
-Proficient in SQL and at least one programming language (Python or R).
-Hands-on experience developing or prototyping AI use cases, including prompt design, evaluation of AI outputs, and simple workflows.
-Ability to design evaluation frameworks for AI outputs, including defining metrics, test cases, and guardrails.
-Familiar with data structures and modelling required to support AI-driven use cases.
Product Management
-Proven product management skills spanning roadmap development, requirements gathering, UX design, and go-to-market execution.
-Demonstrated ability to drive products from concept to pilot to production, translating learnings into structured outcomes.
-Comfortable balancing strategic thinking with hands-on execution in early-stage product environments.
Professional Attributes
-Structured and rigorous, bringing clear frameworks and logic to complex or ambiguous problems.
-Proactive in identifying gaps and opportunities, with strong stakeholder communication and alignment skills.
-Able to work independently in evolving environments while owning workstreams end to end.
-Collaborative, hands-on, and disciplined — balancing exploration with follow-through and a strong focus on end-user value.
Additional Qualifications
Experience in tourism, hospitality, or the public sector is advantageous. Familiarity with AWS QuickSight or similar cloud analytics platforms, data integration processes, API management, and AI application development (including prompt engineering or LLM evaluation) would be a plus. Relevant certifications in product management, data engineering, or AI/ML are welcome.
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