• Serve as the onsite operational lead for large-scale data center deployment projects. • Coordinate and manage multiple concurrent deployment workstreams, ensuring projects remain on schedule and within defined objectives. • Provide onsite leadership, direction, and communication between internal teams, customers, vendors, and technical resources. • Oversee server, rack, and infrastructure deployment activities within enterprise data center environments. • Manage daily project execution, including resource coordination, task prioritization, issue escalation, and progress reporting. • Track project milestones, identify potential risks, and implement solutions to maintain successful delivery. • Validate installation quality, adherence to procedures, and completion of deployment activities. • Support inventory management, logistics coordination, and equipment tracking throughout deployment engagements. • Create and maintain project documentation, status updates, and operational reports. • Ensure all onsite activities follow established safety, security, and data center operational standards.

• Your mission is to turn Neon's raw consumer audio streams into the cleanest, most reliable training data on the market, and to build the commercial and operational engine that gets it into the hands of the world's leading AI labs. • As a Data Ops Lead, you'll own the end-to-end journey that takes raw recordings from our growing community of 500,000+ mobile users and delivers production-ready datasets to frontier labs. • In practice, that means three things above all: - Structuring and managing the data deals that turn our recordings into revenue - Holding every dataset to a quality bar that keeps buyers coming back - Standing up human transcription, annotation and other operations, largely overseas, that make it all possible • You'll work directly with our CEO on commercial priorities and help shape each deal, interface with buyer-side engineering and research teams at frontier labs to translate their exact specifications into deliverable dataset plans, and partner with internal engineering and external vendors to make sure the pipeline supports what we've sold. This is a foundational role: the datasets and processes you build are the product we sell.

• Own the overall CAD system architecture across cooling and power platforms • Define and maintain modular design strategies that enable reuse, scalability and isolation of change across product families • Architect model structures that minimize downstream rework by anticipating growth, option expansion and regional variations • Actively identify and eliminate sources of recurring design churn through architectural refinement • Define and enforce variant logic supporting differences in capacity and ratings, redundancy schemes, regional standards • Establish and maintain CAD modeling standards, structure rules and best practices across all cooling and power systems • Serve as the primary technical authority during system level and cross domain design reviews • Work closely with ME CAD, EE CAD, and PLM/configuration roles to ensure architectural intent is properly implemented

• Be part of a high-impact data science team building intelligent systems that support sales execution and customer engagement at a global scale. • Design, develop, and deploy machine learning models and optimization solutions across the full lifecycle — from research and experimentation to production — focusing on customer segmentation, visit planning, and execution strategy. • Apply advanced techniques such as statistical modeling, clustering, optimization, and model explainability to generate actionable insights and improve decision-making. • Translate complex commercial and operational problems into scalable data science solutions, incorporating business rules, constraints, and edge cases. • Lead and contribute to experimentation and performance evaluation, ensuring models are robust, interpretable, and aligned with business objectives. • Write production-grade code and build reusable data and modeling pipelines that operate reliably at scale. • Collaborate closely with engineers, product managers, operations teams, and business stakeholders to ensure solutions are effectively integrated into frontline tools and processes. • Drive technical excellence by exploring and applying state-of-the-art methodologies in machine learning, optimization, and analytics. • Ensure model transparency and trust by leveraging explainability techniques and clearly communicating model behavior and trade-offs to stakeholders.