• Own the scaling playbook for Bedrock’s Fleet & Data Operations from 6 active states to 15+ — including org structure, SLA design, and in-house vs. dealer/3P decisions. • Develop and manage structured programs with CAT dealers, independent service providers, and technology partners across the East Coast and beyond, defining scope, performance standards, and accountability frameworks. • Evaluate and onboard new vendors and distributor service arms; build the contractual and operational scaffolding (SLAs, rev-share structures, escalation paths) for sustained performance at scale. • Serve as Bedrock’s primary operational point of contact for fleet partner relationships — including advanced technology groups at major CAT and OEM dealer networks. • Translate lessons from dealer-ecosystem SLA and subscription service models into Bedrock’s upfit and maintenance operations. • Provide field supervision for autonomy kit installations (upfit and de-kit) on customer CAT iron, ensuring every install is safe, repeatable, and fully reversible per Bedrock specification. • Lead commissioning and acceptance processes at customer job sites coordinating Bedrock technicians, dealer service teams, and GC site personnel. • Supervise and coordinate the rollout of new technology, tools, and process changes in the field; serve as the operational agent ensuring change lands correctly before scale-out. • Drive new systems and software implementations into the field workflow including fleet management platforms, data collection tooling, and service management systems from pilot through full deployment. • Troubleshoot and resolve escalations on machine uptime, data pipeline integrity, and install quality; own the loop back to engineering with structured field observations. • Own and drive safety initiatives for the department to ensure technicians are safe and in alignment with Partner expectations and construction industry standards. • Guarantee terabyte-scale data collection per machine per day defining processes and partner accountabilities to ensure engineering’s evaluation engine never goes dark. • Set and manage machine uptime and turnaround SLAs across a distributed fleet; design the operational model (staffing, parts logistics, escalation) to meet them as headcount and geography expand. • Build and run the operating cadence (WBR/MBR/QBR) for fleet performance: define KPIs, own the dashboards, drive the corrective actions. • Partner with Engineering and Product to translate uptime and data quality requirements into field-executable standards and partner contracts. • Manage a distributed team of mobile fleet technicians across the East Coast deployment footprint, with accountability for hiring bar, onboarding rigor, and field performance standards. • Design the team structure, routing, and dispatch model to balance machine uptime response against the economics of a field workforce spread across multiple states and job sites. • Partner with Commercial on customer-facing commitments; translate GC and hyperscaler expectations into operational requirements the fleet team can execute reliably with Partners. • Mentor and develop field team leads & supervisors; build the management layer that keeps performance consistent as the team grows. • Experience in an Advanced Technology Group (ATG) or construction technology P&L within a major CAT dealer, OEM, or heavy equipment distributor. • Background in autonomy, machine control, or connected fleet solutions • Familiarity with fleet data systems telematics platforms, data pipeline tooling, or IoT-adjacent infrastructure for heavy assets. • Experience structuring and executing in-house vs. 3P build decisions for field service operations at scale. • Prior experience with hyperscaler or large GC customer relationships in a technical operations or account role. • Understanding of machine lifecycle economics: upfit → operate → de-kit → redeploy, including reversibility requirements and customer asset protection.

• Support the design and implementation of CLEAR’s data architecture. • Partner with developers and data engineers to design and stand up the environment needed to train and fine-tune models (including data ingestion pipelines, compute and GPU resources, experiment tracking and MLOps tooling) actively shaping that environment rather than waiting for it to be provided. • Develop machine learning and deep learning models that integrate multiple data streams to detect early indicators of humanitarian crises, combining earth observation and satellite imagery, conflict event data, climate monitoring, economic indicators, and population movement patterns into unified risk assessment frameworks. • Build computer-vision and remote-sensing models on satellite and aerial imagery (for example flood-extent mapping, building and settlement detection, displacement-site monitoring, infrastructure damage assessment and land-cover change detection) and fuse these earth observation outputs with non-imagery signals. • Contribute to building automated alert systems that identify emerging crises, calibrating prediction algorithms for different crisis types. • Create ensemble modelling approaches that combine traditional statistical methods with advanced AI techniques. • Fine-tune and adapt foundation models and large language models to humanitarian use cases such as document triage, multilingual report analysis and situation summarisation. • Explore venues for adapting models to evolving crisis conditions through reinforcement learning systems. • Implement impact-based forecasting systems that translate meteorological, conflict, and economic predictions into specific humanitarian consequences such as displacement volumes, food insecurity levels, and infrastructure damage estimates. • Build decision trees and recommendation engines that guide field staff through systematic needs assessment processes informed by predictive analytics and historical response data. • Create automated reporting systems and interactive dashboards that enable field teams and leadership to access real-time data for rapid response activities.

• Design, develop, and maintain machine learning models • Build and deploy ML pipelines on AWS • Develop advanced NLP solutions • Design and productionize RAG systems • Develop LLM-powered applications • Implement predictive analytics and statistical modeling • Perform data mining and exploratory data analysis • Build data visualizations and dashboards • Evaluate model performance and present results • Collaborate in an Agile environment

• Providing advanced analytics, ML model design, technical leadership. • Collecting, analyzing and interpreting large data sets to identify trends, patterns, and provide key business insights. • Performs data mining, cleaning, and aggregation processes to prepare data, implement data models, conduct analysis, and develop databases. • Develops insights and reports from multiple structured and unstructured data sources using programming, statistical, and analytical techniques and tools. • Maintains continuous collaboration with teams to understand the underlying purpose, focus, and objective of each data analysis project to ensure alignment and support. • Designs, develops, and implements the most valuable data-driven solutions for the organization. • Works autonomously, but may provide a leadership role for the work group through knowledge in the area of specialization.