SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Yassir is a marketplace platform where millions of daily decisions determine outcomes: driver-to-trip matching, dynamic pricing, delivery ETAs, fraud detection, and credit qualification. This Senior Product Manager role owns the machine learning systems that power these core business decisions and is accountable for their measurable impact.
You will lead data-driven discovery to identify and quantify ML opportunities across the marketplace. This involves diagnosing where value is lost (supply-demand imbalance, cancellations, ETA errors, fraud, defaults) and translating findings into a prioritized problem backlog. You will frame business problems as well-posed ML problems: defining prediction targets, decision contexts, units of analysis, label quality, and error costs. You'll know when a rule or heuristic suffices instead of a model.
Metrics and success criteria are central to your work. You will define the chain from model metrics (precision, recall, calibration, MAE) to product and business metrics, own guardrail metrics, and understand why offline gains often fail online. You will design and interpret controlled experiments in two-sided marketplace settings where standard A/B tests are unreliable, using switchback, geo, or cluster-randomized designs. You'll reason about statistical power, novelty effects, and heterogeneous impact across cities and segments, and distinguish correlation from causation using appropriate quasi-experimental methods.
You own the full ML product lifecycle: from problem framing through data requirements, baseline development, iteration, launch, monitoring, and retirement. You'll partner with ML engineers and data engineers on rollout strategy, model monitoring, drift detection, retraining cadence, and failure modes. You treat production models as products that degrade without attention.
You will own the ML product roadmap across domains and countries, making explicit trade-offs between accuracy, latency, cost, interpretability, fairness, and regulatory requirements—especially critical in credit and financial services. You'll make ML impact legible to non-technical leadership, communicate results (including null and negative findings) with rigor, and align product, operations, risk, and marketing partners on shared objectives. You'll raise the bar on data-driven decision-making, mentor peers, and build a culture of feedback and trust.
This role focuses on predictive and decisioning ML: ranking, matching, forecasting, pricing, risk, and personalization. LLM or agentic experience is welcome but not a substitute for fundamentals. If your ML product experience is primarily integrating third-party models or APIs into user-facing features, this role is not the right fit.
REQUIREMENTS:
- 4+ years of product management experience, including at least 2 years owning ML-driven products in production that influence core business decisions (pricing, matching, ranking, forecasting, fraud, credit risk, recommendations).
- Hands-on fluency with data: write SQL, explore data independently, and challenge analyses without waiting for others.
- Demonstrated experience designing, running, and interpreting controlled experiments with solid working understanding of causal inference.
- Strong understanding of core ML concepts: supervised learning, evaluation metrics and trade-offs, overfitting and leakage, bias, calibration, and offline-to-online performance relationships.
- Track record of shipping ML products where you can clearly articulate problem framing, metrics chosen, experiment design, and measured business impact (including failures).
- 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 related quantitative field.
