Product Manager - Data & AI

About the role

Soum is a fast‑growing B2C marketplace in Saudi Arabia. The Data & AI Product Manager will turn transaction data into a competitive advantage by owning pricing, fraud detection, inspection automation, and personalization products.

Key responsibilities

• Define and own the Data & AI product roadmap tied to commercial outcomes such as GMV, margin, conversion, operating cost or fraud loss.
• Lead pricing and valuation products for used electronics and cars, including instant quotes, price recommendations and dynamic pricing.
• Identify, size and ship AI/ML use cases across the business (search ranking, recommendations, fraud scoring, image‑based grading, LLM‑powered listing creation, bilingual support automation).
• Own the data platform as a product: collaborate with data engineering on pipelines, models, warehouse and event tracking, ensuring data quality, lineage and documentation.
• Stay hands‑on with data: write SQL, explore datasets, specify tracking schemas, review data models and validate business needs.
• Translate business problems into ML problem statements, define metrics, success criteria and guardrails with data scientists and ML engineers.
• Build business cases, make build‑vs‑buy decisions and monitor models post‑launch for performance, drift and impact.
• Ensure responsible and compliant use of data and AI in line with Saudi Arabia's PDPL and SDAIA guidance.

Required profile

• 2‑4 years of product management experience focused on data platforms, ML/AI or data‑intensive products.
• Advanced SQL skills and deep understanding of data modelling (dimensional/star schema), ETL/ELT, event tracking and modern data stacks (BigQuery, Snowflake, Redshift, dbt, Airflow, Kafka).
• Solid grasp of the ML lifecycle, GenAI, feature engineering, model evaluation, MLOps and LLM techniques (RAG, prompt design, vendor APIs).
• Proven ability to link data or AI products to revenue, margin or cost outcomes and to ship them into production with measurable impact.
• Strong commercial acumen, independent decision‑making and excellent communication across engineering, data science and commercial teams.
• Fluent English; Arabic is a plus.

Required skills

• SQL
• Data modelling (dimensional/star schema)
• BigQuery, Snowflake, Redshift
• dbt
• Airflow
• Kafka
• ML lifecycle & MLOps
• Generative AI (LLM, RAG, prompt design)