Senior Product Manager – Machine Learning

Yassir

. Identify and size ML opportunities through data-led discovery .

Posted 10/7/2026full-timeCairo • EgyptSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in product management for machine learning applications, including the ability to define metrics, design experiments, and communicate results effectively to stakeholders. Proficient in SQL and experienced in managing ML product roadmaps while balancing technical and business considerations.

Highest-signal resume keywords

Product Management ExperienceMachine Learning Product OwnershipSQL ProficiencyControlled Experiment DesignCausal Inference Understanding

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning ConceptsSupervised LearningEvaluation MetricsData AnalysisExperiment DesignProblem FramingQuantitative AnalysisModel MonitoringDrift DetectionMetric Definition

Soft Skills

MentoringCommunicationCollaborationFeedback CultureTrust Building

Certifications & Qualifications

BSc in EngineeringMSc in Computer ScienceMSc in StatisticsMSc in Data Science

Industry Keywords

MarketplacesMobilityOn-Demand DeliveryFintechData-Led Discovery

Tech Stack

Tools & technologies

SQL

About the role

Key responsibilities & impact

Identify and size ML opportunities through data-led discovery
Diagnose marketplace value loss and create a prioritised, quantified problem backlog
Frame business problems as well-posed ML problems and determine when rules or heuristics are more appropriate
Define model, product and business metrics, including guardrail metrics
Design and interpret controlled experiments and quasi-experimental methods in two-sided marketplaces
Own ML products from framing through data requirements, baseline, iteration, launch, monitoring and retirement
Partner with engineering on rollout strategy, model monitoring, drift detection, retraining cadence and failure modes
Own the ML product roadmap across domains and countries
Balance accuracy, latency, cost, interpretability, fairness and regulatory requirements
Communicate ML impact, including null and negative results, to non-technical leadership
Align product, operations, risk and marketing partners on shared objectives
Mentor peers and build a culture of feedback and trust

Requirements

What you’ll need

4+ years of product management experience, including at least 2 years owning ML-driven products that run in production and influence core business decisions (e.g. pricing, matching, ranking, forecasting, fraud, credit risk, recommendations)
Hands-on fluency with data: ability to write SQL, explore data independently and challenge an analysis
Demonstrated experience designing, running and interpreting controlled experiments, and a solid working understanding of causal inference
Strong understanding of core ML concepts: supervised learning, evaluation metrics and their trade-offs, overfitting and leakage, bias, calibration, and the relationship between offline and online performance
Track record of shipping ML products and articulating problem framing, chosen metrics, experiment design and measured business impact, including what went wrong
Experience working with distributed or remote teams across multiple markets
BSc/MSc in Engineering, Computer Science, Statistics, Data Science or a related quantitative field
Experience in marketplaces, mobility, on-demand delivery or fintech is a strong plus