Data Scientist II, Product - Moloco Commerce Media

The Impact You'll Be Contributing to Moloco:

Moloco Commerce Media (MCM) enables marketplaces to leverage recent advances in AI to deliver highly relevant ads powered by first-party data. MCM has established strong product-market fit and is a key growth engine for Moloco as we expand our footprint with leading global and regional commerce platforms.

As a Data Scientist on the MCM Data Science team, you will partner closely with Product, Engineering, and Go-To-Market teams to shape how MCM defines success, evaluates what it ships, and decides where to invest next. You will work alongside product managers from the earliest stages of a new capability through experimentation, readout, and the decisions that follow. Your work will directly inform which features MCM builds, how we know they work, and how we communicate their value to leadership and marketplace partners.

The Opportunity:

In this role, you will:

• Partner with Product on strategy: Act as an independent thought partner to product managers, proactively surfacing opportunities, sizing them with data, and informing roadmap prioritization. You will bring your own point of view on what to build and why.
• Define success metrics for new product initiatives: Establish how MCM measures new capabilities, including north star and guardrail metrics, the trade-offs they encode, and the thresholds that guide ramp decisions. Align Product and Engineering on these frameworks before launch.
• Drive evaluation and experimentation: Design experiments and evaluation methodologies for product launches, including cases where standard A/B testing is insufficient. Analyze results rigorously and translate them into clear ship, iterate, or stop recommendations.
• Conduct deep-dive analyses: Use large-scale data across our ad-tech stack (ranking, retrieval, bidding, pacing) to understand how product changes affect advertiser outcomes and marketplace dynamics, and turn findings into actionable recommendations.
• Communicate insights clearly: Distill complex analyses into narratives that product, engineering, and non-technical stakeholders can act on. Deliver launch readouts and contribute the data narrative to product reviews.
• Uphold data science excellence: Apply best practices in analysis, modeling, and experimentation, and contribute to a strong data culture within the MCM team.

How Do I Know if the Role is Right For Me?

• You want to shape products, not just measure them, and are comfortable speaking up when the data disagrees with the plan.
• You operate independently in ambiguity, turning an open-ended product question into a structured analytical plan without waiting for a spec.
• You are energized by fast-moving, early-stage product work, where metrics do not yet exist and evaluation methods have to be invented.
• You are a strong collaborator who builds trust with product, engineering, and commercial stakeholders.

Minimum Qualifications:

• 3+ years of industry experience in data science, analytics, or a related field (or an advanced degree with equivalent applied experience).
• Bachelor's, Master's and/or PhD in a quantitative discipline (e.g. Statistics, Operations Research, Economics, Mathematics, etc.).
• Experience partnering with product or business stakeholders: a track record of defining success metrics, running experiments, and influencing decisions with data.
• Strong proficiency in Python and SQL, with experience analyzing large-scale datasets.
• Solid foundation in statistics, including experimental design, hypothesis testing, and various statistical analysis techniques such as regression.
• Excellent communication skills with experience presenting technical findings to non-technical stakeholders.
• Ability to build strong relationships and collaborate effectively in a global, cross-functional organization.

Preferred Qualifications:

• Experience in ad-tech, marketplace, or e-commerce environments (specifically bidding, ranking, or auction logic).
• Experience evaluating machine learning or AI-driven systems in production, including offline evaluation, online experimentation, and ongoing performance monitoring.
• Familiarity with causal inference methods or evaluation approaches for products where standard A/B testing does not apply.
• Experience working with geographically distributed teams and stakeholders.