Why this role exists:
The Associate Product Data Optimisation Analyst exists to enrich product data at scale using customer, competitor, supplier, and performance insights to enhance customer decision-making across all digital touchpoints. The role designs, deploys, and governs AI-assisted workflows for product attributes, tagging, classification, and content recommendations, creating value by enriching more products faster while reducing manual data work.
Operating as a technical optimisation specialist within the Commercial function, the role bridges product data management with advanced analytics, machine learning, and automation. By identifying and closing Product Detail Page (PDP) and Product Listing Page (PLP) information gaps, automating data onboarding, and maintaining strict human-in-the-loop quality controls, the role directly improves product discoverability, page conversion, and customer trust
Where you will make a difference:
In this role you will:

Large-Scale Product Data Enrichment:
Extract and synthesise customer, competitor, supplier, and e-commerce performance insights to enrich core product data.
Identify and resolve critical information gaps across Product Detail Pages (PDP) and Product Listing Pages (PLP) to elevate customer decision-making.
Structure taxonomy definitions, attribute frameworks, and information architecture across digital catalogue ranges.
Translate customer search behaviour, ratings, reviews, Q&A;, and competitor market signals into actionable enrichment priorities.
Monitor product data completeness across priority assortments to ensure high-quality, customer-ready digital experiences.
AI-Assisted Workflow Design and Deployment:
Design, test, and deploy generative AI, machine learning, and natural language processing (NLP) models to automate product content generation.
Build automated pipelines for attribute extraction, product tagging, category classification, and smart content recommendations.
Develop repeatable, production-ready data prototypes and scalable enrichment rules rather than isolated analytical scripts.
Establish prompt engineering guidelines and model parameters to maintain brand voice, factual accuracy, and content compliance.
Leverage SQL, Python, APIs, and cloud-data platforms to integrate AI models seamlessly into enterprise workflows.
Data Onboarding and Exception Automation:
Automate end-to-end product data onboarding, validation routines, and data quality monitoring frameworks.
Engineer automated exception detection tools to flag missing attributes, specification variances, or formatting errors before customer impact.
Maintain PIM and Adobe Analytics integrations to streamline data ingestion and monitor digital content performance.




Establish explicit exception ownership models to ensure data anomalies are rapidly investigated and remediated.
Streamline manual data processing tasks to release operational capacity across category and content teams.
AI Governance and Model Quality:
Establish human-in-the-loop governance frameworks to review, validate, and audit AI-generated product content.
Monitor model accuracy, first-pass acceptance rates, and content quality thresholds to prevent hallucinations or data drift.
Adhere to AI governance, data privacy standards, and IP compliance across all automated content workflows.
Report model performance metrics, quality exceptions, and compliance risks to AI governance and legal stakeholders.
Champion responsible AI adoption and ethical data usage across commercial and product teams.
Test-and-Learn Optimisation and Platform Roadmap:
Design and execute A/B testing and experimentation routines to measure the commercial impact of enriched product data.
Track conversion rate uplift, search visibility improvements, and customer engagement metrics stemming from data enhancements.
Embed scalable data quality controls and continuous improvement mechanisms across product information lifecycles.
Partner with technology and platform vendors to influence future capability roadmaps for PIM, AI, and analytics tools.

Who you will be working with
Category and Commercial Partners: Partner with Category Management, Category Planning, Customer Experience, Digital, and Marketing teams to identify and prioritise high-value data enrichment opportunities.
Technology and AI Governance Partners: Collaborate with Technology and Data, Data Science, AI Governance, Legal, Risk, and Compliance teams to design, test, and govern controlled AI solutions.
Analytics and Content Partners: Work alongside Product Data Writers, E-commerce Analysts, and Web Development teams to translate analytics into production-ready enrichment rules.
External Vendor Partners: Liaison with platform, data, and technology partners (including STIBO/PIM, Adobe Analytics, Bazaarvoice, AI technology partners, and market data providers) to manage system integrations and tools.
What success looks like:
Assortment Enrichment Quality: High percentage of priority product assortments meeting enterprise completeness, taxonomy, and enrichment quality standards.
Commercial Conversion Impact:



Measurable uplift in PDP and PLP conversion rates, organic search visibility, and customer engagement resulting from data enhancements.
Process Efficiency and Cycle Time: Significant reduction in product data cycle times and user operational hours released through automated workflows.
AI Accuracy and Governance: High first-pass acceptance rate for AI-generated enrichment with robust human review controls and zero compliance exceptions.
Demonstrates behaviours aligned to Officeworks values, contributing to a respectful, inclusive and high-performing culture
How you will lead:
Individual Contributor:
Lives our Officeworks values and behaviours
Proactively contributes to a safe working environment, escalates appropriately if there are unsafe conditions or inappropriate behaviour
Operates in line with applicable Officeworks company policies and Code of Conduct
Demonstrates a strong sense of personal accountability and curiosity to learn and develop
Contributes to an inclusive and respectful environment where diverse perspectives are valued and everyone feels safe to speak up and belong

Qualifications and work experience:
Essential:
Education: Tertiary qualification in Data Science, Analytics, Information Systems, Computer Science, Digital Commerce, or a related discipline (or equivalent practical product-data and eCommerce analytics experience).
Experience: 35+ years of experience in product data management, e-commerce analytics, data science, process automation, or digital optimisation roles.
Technical Mastery: Strong technical capability in SQL, Python, API integrations, and workflow automation for data processing.
AI and NLP Application: Practical experience applying generative AI, machine learning, or natural language processing (NLP) to product data and digital content workflows.
System Proficiency: Proficiency with enterprise Product Information Management (PIM) platforms (e.g., STIBO/PIM), Adobe Analytics, and data visualisation tools.
Data Architecture Knowledge: Advanced understanding of product data enrichment, retail taxonomy, attribute modelling, and digital information architecture.
Governance and Compliance: Understanding of AI governance frameworks, human-in-the-loop review mechanisms, data privacy, model quality monitoring, and content compliance
Preferred:
Professional certifications in AI, Machine Learning, Cloud Data Platforms (e.g., AWS/GCP/Azure), SQL, or Python.
Advanced hands-on experience with STIBO/PIM, Adobe Analytics, Bazaarvoice, or specialised A/B testing and experimentation platforms.
Demonstrated experience in omnichannel retail, large-scale consumer e-commerce, or marketplace environments

📌 Associate Product Data Optimisation Analyst (Bengaluru)
🏢 Officeworks
📍 Bengaluru