Product Manager - Data & AI

Key Responsibilities

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- 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.