Senior Applied Scientist, Moloco

• Ph.D. in Statistics, Economics, Mathematics, Operations Research, or another quantitative field with exposure to advanced modeling and statistical techniques
• Proficient in Python and SQL. Experienced working efficiently with large datasets, including model prototyping and statistical simulations
• Deep understanding of experimental design, causal inference and advanced statistical methods
• Excellent verbal and written communication skills, with the ability to communicate complex technical concepts to non-technical stakeholders
• Ability to build positive relationships within the DS team and with our stakeholders, and work effectively with cross-functional partners in a global company
• Willingness to both teach others and learn new techniques

Desirable

• Moloco Commerce Media (MCM) enables marketplaces to leverage the recent advances in AI for delivering tailored ads to relevant users, driven by first-party data. We’ve established our MCM’s product market fit with strong interest in our product. MCM is an important product that is key to Moloco’s continued success and is in a high growth phase
• The Data Science & Applied Science team supports MCM's growth by supporting the launch of new features through modeling, algorithmic and analytical excellence
• As a Senior Applied Scientist focused on Inference, you will be the primary architect of our experimentation platform and will develop the statistical methodologies that power business-critical decisions across the entire MCM product
• You will have the opportunity to work on and improve various components of our ad-tech stack including ranking, retrieval, auction/bidding and budget pacing
• Methodology Development: Develop and implement advanced statistical methodologies (e.g., variance reduction techniques, such as CUPED) to improve the validity, power, and generalizability of MCM experiments as the primary owner of MCM’s experimentation platform
• Platform Innovation: Partner with Product and Engineering to drive the development of an automated experimentation platform from ideation to productionalization. Identify opportunities to improve platform functionality like automated power analysis
• Cross-Functional Leadership: Guide and assist partner teams (ML, Product, and Sales) through the experimentation lifecycle. Help them define the right business questions, choose appropriate metrics, and interpret results
• Culture & Education: Act as the internal champion for experimentation best practices. Educate peers on statistical pitfalls (e.g., network effects, p-hacking) through documentation, presentations, and mentorship
• Strategic Deep Dives: Use Python/SQL and work at scale with large datasets to conduct analysis and identify improvements to our ML models. Validate and quantify the efficiency and performance gain from hypotheses with advanced statistical methods and rigorous A/B testing