Senior Product Manager - Machine Learning

Team: Data

Location: Cairo, Egypt, Alexandria, Egypt, India, Remote, remote, Lisbon, Greece, Bucharest, Belgrade

Commitment: Full-time

Workplace Type: hybrid

About the Role

Yassirs marketplace runs on decisions made millions of times a day: which driver gets which trip, what a ride should cost, when an order will arrive, which transaction looks fraudulent, who qualifies for credit. We are looking for a Product Manager who owns the machine learning systems behind those decisions and is accountable for their measurable impact on the business.
This is a product role for ML-heavy, data-intensive products. You will lead discovery from data rather than from opinion, frame business problems as problems a model can actually solve, define what success means both offline and in production, and prove impact through well-designed experiments. You will work closely with data scientists, ML engineers and data engineers, and with product and operations partners across ride-hailing, delivery and financial services.
 
What This Role Is (and Isnt)

This role centres on predictive and decisioning ML: ranking, matching, forecasting, pricing, risk and personalisation. Experience building LLM or agentic features is welcome, but it is not a substitute for the fundamentals below. If your ML product experience is primarily integrating third-party models or APIs into user-facing features, this role is likely not the right fit.

Responsibilities

Data-led discovery: Identify and size ML opportunities by going into the data yourself. Diagnose where the marketplace is losing value (supply-demand imbalance, cancellations, ETA error, fraud losses, default rates) and translate it into a prioritised, quantified problem backlog.
Problem framing: Turn business problems into well-posed ML problems: define the prediction target, the decision it informs, the unit of analysis, label availability and quality, and the cost of different error types. Know when a problem does not need ML and a rule or heuristic will do.
Metrics and success criteria: Define the chain from model metrics (e.g. precision/recall, calibration, MAE) to product and business metrics, and own guardrail metrics. Understand why offline gains often fail to translate online, and plan for it.
Experimentation and causal inference: Design and interpret controlled experiments, including in two-sided marketplace settings where interference makes standard A/B tests unreliable (switchback, geo or cluster-randomised designs). Reason about statistical power, novelty effects and heterogeneous impact across cities and segments. Distinguish correlation from causation, and know which quasi-experimental methods to use when randomisation isnt possible.
ML product lifecycle: Own models 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. Treat a model in production as a product that degrades if unattended.
Roadmap and trade-offs: Own the ML product roadmap across domains and countries. Make explicit trade-offs between accuracy, latency, cost, interpretability, fairness and regulatory requirements, especially in credit and financial services.
Stakeholder alignment and visibility: Make the impact of ML work legible to non-technical leadership. Communicate results, including null and negative results, with rigour and clarity. Align product, operations, risk and marketing partners on shared objectives.
Leadership and culture: Raise the bar on how the organisation makes decisions with data. Mentor peers, build a culture of feedback and trust, and invest in your own growth and that of those around you.

Requirements

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: you can write SQL, explore data independently and challenge an analysis without waiting for someone else to run it.
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.
A track record of shipping ML products where you can clearly articulate the problem framing, the metrics chosen, the experiment design and the measured business impact, including what went wrong.
Experience working with distributed or remote teams across multiple markets.
Experience in marketplaces, mobility, on-demand delivery or fintech is a strong plus.
BSc/MSc in Engineering, Computer Science, Statistics, Data Science or a related quantitative field.