Applied Scientist II - Moloco Commerce Media

The Applied Science team in MCM (Moloco Commerce Media) focuses on improving the performance, efficiency, and reliability of Moloco Commerce Media’s marketplace advertising systems, working closely with software engineers, machine learning engineers, data scientists, and product partners. As an Applied Scientist II, you will contribute as an individual contributor on research and optimization projects in a complex, multi-causal environment. You will help identify inefficiencies, analyze trade-offs, and propose practical solutions across core MCM systems such as bidding, pacing, pricing, ranking, experimentation, and launch decisions.

Responsibilities

Work with other applied scientists, software engineers, and data scientists to evaluate the health, efficiency, and performance of MCM’s core marketplace advertising systems; Conduct deep, unbiased analyses on large-scale data to identify inefficiencies, explain performance changes, and uncover root causes across system, model, data, and market changes; Design, implement, and evaluate algorithms, metrics, and analytical frameworks that improve bidding, pacing, pricing, ranking, and experimentation outcomes; Support the launch and ramp of new features by defining success metrics, guiding A/B tests, interpreting results, and making data-driven recommendations; Collaborate closely with Product, Engineering, ML, Data Science, and business stakeholders to translate ambiguous commerce media problems into clear analytical plans and practical solutions; Learn from senior peers how to identify high-leverage opportunities across the MCM system and proactively suggest meaningful next steps for your projects

Requirements

MS or Ph.D. degree in Computer Science, Statistics, Mathematics, Economics, Operations Research, or a related quantitative field; 4+ years of industry or postgraduate experience in applied science, data science, machine learning, experimentation, optimization, or a related analytical field; Strong foundation in statistics, probability, experimentation, causal reasoning, and optimization; Proficiency in Python and SQL for large-scale data analysis and problem solving; Ability to analyze complex multi-causal systems, generate hypotheses, evaluate trade-offs, and communicate clear recommendations; Proficient verbal and written English communication skills, with the ability to contribute to presentations, reports, and cross-functional discussions with global stakeholders; Quick understanding of new information and demonstrated ability to learn technical concepts across engineering, machine learning, and data science; Track record of building positive relationships with collaborators and stakeholders and working effectively with cross-functional partners in a global company