About Moloco

Moloco is an AI-native performance advertising company, bringing the power of AI advertising to the open internet. Founded in 2013 by machine learning engineers who had built AI advertising systems inside the world's largest technology companies, Moloco began from a shared mission: to build that same engine for the open internet.

Today, Moloco is one company with two businesses, built on the same compound AI system. Moloco Ads is our AI-native performance advertising platform, helping mobile app marketers acquire, retain, and re-engage high-value users across the open internet. Moloco Commerce Media is our AI-native retail media platform, enabling retailers and marketplaces to turn their platforms into thriving ad businesses. Both are powered by CARA (Compound Ad Recommendation Architecture), the AI system behind every Moloco product.

At Moloco, we bring together those who've already solved hard problems and those with the drive to. Here, we work on some of the complex problems in AI advertising, at a scale and level of sophistication very few companies can offer. Come build what's next.

The impact you'll be contributing to Moloco:

Product Analytics Data Science (PDS) is the data science team that works closely with the R&D team on our core Ads product. As a Senior Applied Scientist, you will drive performance improvement in our product through a deep understanding of the ads ecosystem (supply, demand, bidding, ML) and customer needs, and provide the data driven insight and scientific solutions.

Here’s what you’ll be working on

Our team is responsible for:

Conducting deep-dive analysis to influence R&D decisions and strategies: analyzing large datasets with queries and scripts, extracting valuable signals out of noise, and producing actionable insights into how we could improve Moloco’s complex ML and bidding system to drive outcomes for customers.
Building the right metrics to measure campaign health: improving campaign performance monitoring by establishing the right metric framework, and understanding cause-and-effect relationships across drivers
Evaluating ads system health: understanding changes and drivers of our complex systems, and diagnosing issues through a set of input/output metrics of each component.

Here’s what you’ll be working on

Work with other applied scientists on projects to evaluate the health of both internal and external components to make sure the Moloco system is running safely and efficiently.
Learn from your senior peers how to identify new areas to improve our infrastructure and machine learning components by understanding internal system changes, external changes, and data changes, and identify and suggest interesting/useful next steps for your projects.
Complete task as part of larger projects to complete deep unbiased analyses.
Implement, and evaluate new algorithms and features in collaboration with senior Applied Scientists, Software Engineers and Machine Learning Engineers.
Analyze massive, high-dimensional datasets from ad auctions and user interactions to uncover insights, define success metrics, and guide modeling and product decisions.
Collaborate closely with product, engineering, and other scientists to iterate on experiments, define roadmaps, and translate business requirements into scientifically sound solutions.
Improve the reliability, latency, and efficiency of online prediction and decision systems by applying rigorous experimentation, monitoring, and continual model refinement.

Basic Qualifications

Advanced degree in Computer Science, Mathematics or related field, or industry experience in optimization of market prices or ML systems
At least 4 years of engineering or applied science experience
Proficient verbal and written English communication skills, with the ability to contribute to the creation of presentations and reports
Quick understanding of new information and the demonstrated ability to learn new technical skills 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