• Develop and execute go-to-market strategies for new product launches, updates, and feature enhancements, with a strong focus on AI workflows and real-world use cases. • Craft narratives that clearly communicate the value of Tellius’ AI-driven analytics in practical terms, tailored to various audiences including data scientists, analysts, and business decision-makers. • Produce technical marketing collateral such as solution briefs, whitepapers, video demos, and blog posts that showcase how Tellius enables advanced AI workflows. • Collaborate with sales, customer success, product management, and data science teams to gather customer feedback, ensuring that our messaging aligns with customer needs and highlights practical applications of AI. • Continuously monitor market trends and analyze competitors’ offerings to differentiate Tellius’ AI capabilities. • Partner with sales to develop training materials, pitch decks, and objection-handling resources that effectively communicate Tellius’ value proposition in AI analytics. • Represent Tellius at industry events, webinars, and customer engagements, serving as both a technical expert and a marketing strategist, focusing on AI workflows and use cases.

• Build AI agents that replace manual research work. • Ship real systems used in production. • Examples of what you might build: AI agents that email organizations and parse responses, voice agents that call organizations to collect missing data, automated workflows that replace human research tasks, and systems that collect proprietary data at scale. • By ~week 2-4: You’ve built an AI agent that replaces a human researcher and collects production-level data at scale.

• Own onboarding, handoffs, and lifecycle playbooks from close to renewal • Define customer engagement models by segment (SMB / Mid-Market / Enterprise) • Ensure clear ownership and accountability at every stage of the lifecycle • Define and own KPIs across all CS functions (adoption, health, churn, NRR, renewals) • Establish weekly and monthly operating cadence and performance reviews • Drive forecasting accuracy for renewals and expansion • Hire, onboard, and scale critical CS roles as the business grows • Coach and performance-manage CSMs, Support, and Solution Engineering • Build a high-accountability, customer-first culture • Own churn prevention strategy and early risk identification • Ensure every at-risk account has a documented mitigation plan • Partner with Sales on expansion readiness, timing, and qualification • Support renewals and expansions with clear value articulation • Partner closely with VP Sales on retention modelling, forecasting, and GTM alignment • Align CS with Product to close feedback loops • Act as the voice of the customer internally • Own CS tooling and data hygiene (CRM, health scoring, reporting) • Ensure consistent documentation, notes, and account plans • Drive operational discipline and eliminate reactive fire drills • Other duties as assigned.

• Partner with automotive OEMs and Tier 1 suppliers to identify, shape, and prioritize high-impact AI-driven use cases, guiding customers toward scalable and high-leverage solutions • Act as a trusted advisor to customers by shaping problem definition, challenging assumptions, and guiding technical decision-making toward scalable AI solutions • Drive alignment and adoption of solutions by clearly articulating trade-offs, ROI, and long-term architectural implications to technical and executive stakeholders • Design, integrate, and deploy AI/ML-based solutions across cloud and in-vehicle environments, enabling end-to-end vehicle-to-cloud use cases with reliable data flow and system interoperability • Develop and iterate AI models, PoCs, and production-ready solutions, continuously improving performance based on real-world data and customer feedback • Lead technical discussions and solution reviews focused on AI feasibility, model performance, trade-offs, and system limitations • Identify risks related to AI deployment and integration (e.g., data availability, security constraints, model reliability) and drive mitigation strategies • Collaborate with internal teams (AI Platform, Product, Engineering) to enhance AI capabilities, model deployment pipelines, and platform scalability