Product Manager - Data & AI

Key Responsibilities
Define and own the Data & AI product roadmap, with every initiative tied to a clear commercial outcome such as GMV, margin, conversion, operating cost or fraud loss. Lead pricing and valuation products for used electronics and cars, including instant quotes for sellers, price recommendations and dynamic pricing based on condition, demand, seasonality and market trends. Identify, size and ship AI/ML use cases across the business, such as search ranking and recommendations, fraud and risk scoring, image-based condition grading, LLM-powered listing creation, and Arabic/English customer support automation. Own the data platform as a product: work with data engineering on pipelines, data models, the warehouse and event tracking, with clear standards for data quality, lineage, documentation and access. Stay hands-on with the data: write SQL, explore datasets, spec tracking schemas and data contracts, review data models, and validate that what is built matches what the business needs. Translate business problems into well-defined ML and analytics problem statements, defining offline and online evaluation metrics, success criteria and guardrails with data scientists and ML engineers. Build business cases and make build-vs-buy decisions (for example third-party LLM APIs versus in-house models), including ROI, inference and infrastructure costs. Drive adoption of data products across Commercial, Operations, Finance and leadership, including self-serve analytics, trusted KPI definitions and dashboards people actually use. Monitor models and data products after launch for performance, drift, data quality and business impact, and iterate quickly when results fall short. Ensure responsible and compliant use of data and AI, in line with Saudi Arabia's Personal Data Protection Law (PDPL) and SDAIA guidance, with appropriate privacy, human-in-the-loop and bias controls. Explain complex technical trade-offs clearly to non-technical stakeholders and leadership, and build alignment around priorities.

Requirements
2-4 years of product management experience, with significant time spent on data platforms, ML/AI products or data-intensive products. Data depth comparable to a data engineer: advanced SQL, data modelling (dimensional / star schema), ETL/ELT, event tracking, and familiarity with modern data stacks (e.g. BigQuery, Snowflake or Redshift; dbt; Airflow; Kafka). Solid understanding of the ML lifecycle and modern GenAI: problem framing, feature engineering, model evaluation, MLOps, and LLM techniques such as RAG, prompt design and working with vendor APIs. Strong commercial acumen, with a track record of linking data or AI products to revenue, margin or cost outcomes and building convincing business cases. Has shipped AI/ML or data products into production and can show measurable impact, not just prototypes. Ability to drive products forward independently: setting direction, unblocking teams, and making fast, well-reasoned decisions. Excellent communication skills, able to move comfortably between engineers, data scientists and commercial leaders. Fluent English; Arabic is a plus.

Nice to Have
Prior experience as a data engineer, analytics engineer or data scientist before moving into product. Experience with pricing engines, marketplaces, e-commerce, automotive, or fintech risk and fraud. Experience with computer vision or Arabic NLP use cases. Familiarity with PDPL, data governance frameworks or the GCC data regulatory landscape.