Senior Product Manager- ML Decision Systems (Demand Side Platform)

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 Senior 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.

• →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.

Experience

• 7+ years of Product Management experience
• 3+ 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

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

• Write structured PRDs
• Analyze performance metrics
• Reason about trade-offs quantitatively

Experience with SQL or basic data analysis tools is preferred.

• 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.

• Experience with GenAI/LLMs and rapid prototyping.
• Experience building ML experimentation frameworks.
• Experience mentoring junior PMs.

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