Senior Applied Scientist, Inference (MCM)

Moloco•Redwood City, CA

1d•$184,000 - $230,000

About The Position

About Moloco: Moloco builds some of the most powerful AI advertising solutions in the world. Our name—short for "machine learning company"—reflects our core mission: democratizing access to the advanced AI that has historically been reserved for tech giants. Led by machine learning pioneers who built some of the most successful ad systems at Google, including YouTube's monetization engine and key search advertising technologies, we're transforming how businesses grow and compete in the digital economy. Built with AI from day one, Moloco’s planet-scale machine learning platform powers a suite of solutions for advertising growth and monetization. Moloco Ads is an AI-powered platform that delivers real business outcomes for mobile app marketers through performance-based user acquisition. Moloco Commerce Media and Streaming Monetization solutions enable retailers, marketplaces, and streaming platforms to build revenue-generating ad businesses that balance user experience and advertiser performance. Moloco is headquartered in Silicon Valley, with offices in Seattle, New York, San Francisco, Seoul, Beijing, Singapore, Bangalore, Gurgaon, Tokyo, Shanghai, London, Tel Aviv, and Berlin. Moloco is a truly rewarding place to work and in an exciting period of growth, which you could be a part of. Join us today and apply now! The Impact You’ll Be Contributing to Moloco: 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.

Requirements

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

Nice To Haves

2+ years of experience (industry or postgraduate) in Data Science, with a heavy focus on experimentation and causal inference in a high-growth tech environment (AdTech preferred).
Prior experience developing experimentation methodology, building experimentation platforms and designing large-scale online experiments.
Demonstrated leadership and self-direction. Able to seek out new business questions and effectively scope projects to improve our ML model performance.

Responsibilities

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.

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