Mars Customer Manager, Wegmans Account, Illinois Remote
Mars
Lead Mars Snacking’s Accelerator account strategy for Wegmans. Drive retailer growth through assortment, merchandising, pricing, promotions, and forecasting.
Open
Quantcast is a marketing, media, and SaaS company focused on digital advertising and audience intelligence. Its AI Suite (Q+) and Audience Graph support measurement, targeting, and programmatic media across connected TV, audio, video, display, mobile, and native channels. Quantcast also develops cookieless advertising solutions and tools for publishers, serving advertisers, agencies, and businesses in sectors such as direct-to-consumer, retail, travel, gaming, finance, and B2B and B2C markets.
Machine Learning Engineer at Quantcast (Hybrid, UK)
Build and improve scalable, real-time machine learning systems that support Quantcast’s AI-driven advertising platform. You will test modeling approaches, optimize high-throughput services, and collaborate with experienced scientists and engineers.
Description
- Develop, implement, test, and troubleshoot machine learning applications
- Enhance global, large-scale systems that handle millions of real-time requests each second
- Conduct experiments to evaluate new machine learning and modeling approaches
- Work with senior scientists and engineers to refine machine learning models
- Produce clear, efficient, maintainable code aligned with software engineering best practices
- Take part in code reviews and offer constructive technical feedback
- Find system bottlenecks and optimize components for greater scalability
- Track relevant advances in machine learning beyond the organization
- Connect academic theory with production-scale systems through mentorship
Requirements
- Up to two years of machine learning or applied statistics experience, including internships or substantial academic work
- Degree in computer science, mathematics, software engineering, or a closely related discipline
- Proficiency in Python, Java, or a comparable programming language
- Solid knowledge of probability, statistics, and hypothesis testing
- Working knowledge of core machine learning methods, such as classification, regression, clustering, ranking, NLP, or LLMs
- Experience with data-processing tools including Pandas and NumPy
- Familiarity with machine learning frameworks such as PyTorch, Scikit-learn, or XGBoost
- Strong interest in distributed systems, software design, concurrency, data structures, and software engineering
Benefits
- Eligibility for a performance bonus
- Equity participation
- Comprehensive benefits package
- Practical mentorship from senior scientists and engineers
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