About the Role
We are building next-generation ML-driven decision systems powering a global Demand Side Platform (DSP). These systems operate at massive scale and optimize real-time marketplace decisions across pricing, targeting, fraud prevention, and performance optimization.
We are looking for a Product Manager who thrives at the intersection of business strategy, machine learning, and real-time systems. This role is ideal for someone who has owned ML-powered products in production environments.
What You Will Own
- Define business objectives and translate them into ML-driven decision systems with direct impact on revenue, margin, auction efficiency, etc.
- Own end-to-end lifecycle of ML initiatives: problem framing, signal validation, modeling alignment, experimentation, rollout, monitoring & iteration.
- Partner closely with Data Scientists and Engineers to ship real-time ML systems at scale.
- Define success metrics, guardrails, and experimentation frameworks.
- Drive auction/pricing/optimization strategies balancing revenue, performance, and risk.
- Own business impact and continuously optimize deployed models.
- Manage cross-functional squads focused on measurable business outcomes.
What We’re Looking For
Experience
- 3+ years of Product Management experience.
- 1+ years owning ML-powered or data-driven products in production.
- Experience in real-time or large-scale decision systems where model outputs directly impacted runtime decisions (e.g., marketplace optimization, pricing engines, ranking systems, fraud models, search/recommendation).
- Experience designing and interpreting A/B experiments.
- Experience working closely with Data Science and Engineering teams.
Strongly Preferred (But Not Mandatory)
- Experience in AdTech, MarTech, digital advertising, or marketplace platforms.
- Exposure to auction systems or dynamic pricing models.
- Experience building products in high-scale consumer or fintech platforms.
- Full-time MBA education.
Technical & Analytical Expectations
You do not need to build models, but you must be comfortable with:
- Prediction targets vs automated decisions.
- Offline vs online metrics (e.g. AUC, calibration, precision-recall).
- Guardrails and business trade-offs.
- Loss functions (conceptual understanding).
- Model drift and retraining cycles.
- Data pipelines and real-time latency constraints.
Ability to:
- Write structured PRDs.
- Analyze performance metrics.
- Reason about trade-offs quantitatively.
Experience with SQL or basic data analysis tools is preferred.
What Makes You a Strong Fit
- High ownership mindset: you own outcomes, not tasks.
- Strong marketplace intuition (supply-demand, incentives, trade-offs).
- Comfortable operating in high-ambiguity environments where signals are imperfect and trade-offs are multi-dimensional.
- Strong cross-functional influence.
- Bias for action and measurable outcomes.
- Comfortable navigating failure and iteration cycles.
Bonus Points
- Experience with GenAI/LLMs and rapid prototyping.
- Experience building ML experimentation frameworks.
- Experience mentoring junior PMs.
Why Join
You’ll work on:
- Core economic engines of a global DSP with direct revenue impact.
- High-scale ML systems powering millions of real-time decisions.
- Marketplace optimization problems with measurable business outcomes.
- Operate with autonomy in a transformational phase of a global DSP platform.