{"id":1165539,"url":"https://alion.io/job/thrive-managed-services-aiautomation-analyst","title":"AI/Automation Analyst","company":{"id":179780,"name":"Thrive Managed Services","domain":"thrivenextgen.com","url":"https://alion.io/company/thrive-3","size_band":"201-500","is_staffing_agency":false,"is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":null},"role":"Analytics","role_family":"Analytics","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":80000,"max_usd":183000,"period":"year","method":"role_country_seniority_unknown","sample_n":2094},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"JavaScript","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"n8n","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Zapier","optional":false},{"name":"Embeddings","optional":true},{"name":"ETL/ELT","optional":true},{"name":"Function Calling","optional":true},{"name":"LLM Evaluation","optional":true},{"name":"RAG","optional":true},{"name":"Structured Outputs","optional":true}],"status":"live","first_seen_at":"2026-09-24T02:42:29Z","employer_posted_date":"2026-09-24","last_verified_at":"2026-09-24T13:20:49Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About the Role\nWe are building AI into the way our go-to-market organization operates, sells, serves customers, and scales. This role exists to identify the highest-value opportunities for AI across the revenue engine, prove the business case with data, and build practical agents and workflows that improve productivity, decision quality, speed, and customer experience.\nThis is a hands-on, business-facing builder role for someone who can operate across Sales, Marketing, Customer Success, and Revenue Operations. You will not simply receive a backlog. You will create one by finding friction, quantifying the opportunity, designing the solution, building and testing it, launching it with the field, and measuring whether it worked.\nThe right person blends GTM judgment, AI fluency, data analysis, and enough technical depth to move from idea to production. This is not an advisory-only role and it is not a research role. It is for someone who can ship useful AI-enabled workflows that real teams adopt.\nWhy This Role Matters Now\nAI is moving from experimentation to operating model. For GTM teams, that means the biggest advantage will not come from isolated tools or demos. It will come from thoughtful, governed workflows that help sellers prepare faster, managers coach with better insight, marketers target with more precision, and customer-facing teams act sooner on risk and opportunity.\nThis role will help define how we responsibly apply AI across the revenue organization, improving scale without simply adding more manual effort or headcount.\nExample Use Cases\nSales meeting prep agents that summarize account context, recent activity, open opportunities, risks, and recommended next actions.\nAccount research workflows that combine internal data and approved external inputs to support prospecting and account planning.\nRenewal and churn risk agents that summarize signals, surface next-best actions, and support executive review processes.\nCRM hygiene and data quality automation, including enrichment, duplicate detection, field validation, and follow-up prompts.\nPost-call and post-meeting workflows that draft follow-ups, update CRM fields, identify action items, and flag coaching opportunities.\nManager insight workflows that summarize pipeline movement, forecast changes, seller activity patterns, and execution risks.\nMarketing and sales alignment workflows that help connect campaign engagement to account prioritization and rep action.\nWhat You'll Do\nFind the opportunities\nWork across marketing, SDR/BDR, sales, customer success, and revenue operations to map how work actually gets done today: the manual steps, the handoffs, the queues, and the places where reps and managers lose time.\nMaintain a living, prioritized inventory of AI and automation opportunities across the GTM funnel, scored on impact, effort, risk, and data readiness.\nPressure-test demand: separate problems that need AI from problems that need a process fix, a field on a record, or a better report.\nBring recommendations forward with a clear thesis: what the problem costs today, what the intervention is, what it will cost, and what changes if we do it.\nUnderstand what drives the need\nPull and analyze the data behind each opportunity: CRM, marketing automation, engagement and sequencing tools, support and CS platforms, product usage, and finance where relevant.\nQuantify the baseline before anything is built: volume, cycle time, conversion, cost per unit of work, error and rework rates.\nDefine the success metric and the measurement method up front, and instrument the workflow so results are observable rather than anecdotal.\nReport on adoption and realized impact after launch, and be candid when something is not working.\nBuild agents and workflows\nDesign and build AI agents and automated workflows across multiple platforms: CRM-native automation, workflow and integration tools, agent-building platforms, and LLM APIs.\nOwn the full build cycle: prototype, test against real data, pilot with a small user group, harden, launch, and iterate.\nWrite and refine prompts, retrieval logic, tool definitions, and guardrails so agents behave reliably in production, not just in a demo.\nHandle the plumbing: data flows, integrations, field mapping, deduplication, error handling, and fallbacks to a human when an agent is out of its depth.\nDocument what you build so it is supportable by someone other than you.\nDrive adoption\nPartner with GTM leaders and frontline teams so what you build gets used: training, enablement materials, office hours, and a feedback loop into the next iteration.\nTreat adoption as part of the deliverable. Workflows must be designed, launched, and measured in a way that makes them useful in the field, not just impressive in a demo.\nKeep it safe and governed\nApply sensible guardrails around data handling, customer-facing output, and human review, particularly anything that touches prospects or customers directly.\nWork with IT, security, and legal on tool evaluation, data access, and vendor review.\nTrack spend across AI tooling and model usage, and keep cost per outcome in view.\nWhat Success Looks Like\nFirst 90 days\nA documented map of GTM processes with the friction points and data gaps called out.\nA prioritized opportunity backlog with sizing and a recommended sequence.\nAt least one automation or agent shipped to production and in daily use.\nFirst year\nA portfolio of agents and workflows live across more than one GTM function, with measured impact on cycle time, capacity, conversion, or cost.\nA repeatable intake-to-launch process others in the org can plug into.\nThe team's default answer to 'could AI do this?' is grounded in evidence rather than opinion, because you supplied the evidence.\nRequirements\n5+ years in revenue operations, sales operations, marketing operations, business systems, data analytics, automation, or applied AI, with strong exposure to GTM workflows.\nDemonstrated hands-on building: you have shipped AI-enabled workflows, automations, agents, or internal tools that real users adopted, and you can explain what you built, why it mattered, and how you measured impact.\nStrong data fluency, including SQL and advanced spreadsheet analysis, with the ability to size an opportunity, define baseline metrics, and build reporting that proves whether the work created value.\nWorking knowledge of the GTM technology stack, including CRM platforms such as Salesforce or HubSpot, marketing automation, sales engagement, conversation intelligence, customer success, support, and reporting tools.\nExperience with LLM APIs, agent-building platforms, prompt design, structured outputs, retrieval or knowledge grounding, tool/function calling, and practical guardrails for production use.\nExperience with automation and integration tooling such as Zapier, Make, n8n, Workato, Tray.io, native CRM flows, webhooks, or API-based integrations.\nComfort with light scripting or technical configuration, preferably Python or JavaScript, APIs, JSON, data transformation, and troubleshooting across systems.\nPractical understanding of evaluation, testing, monitoring, failure handling, privacy, security, and cost management for AI-enabled workflows.\nStrong stakeholder management skills, with the ability to ask good questions, translate between business needs and technical constraints, and challenge requests when the business case is weak.\nSelf-directed and outcome-oriented. This role is defined by what you discover, build, launch, and improve, not only by what you are assigned.\nNice to Have\nExperience in a B2B services, SaaS, MSP, technology, or complex sales environment.\nFamiliarity with RAG, vector databases, embeddings, re-ranking, evaluation harnesses, or AI observability tools.\nExperience with data warehousing, reverse ETL, BI tooling, or analytics engineering.\nExperience supporting sales productivity, sales enablement, forecasting, renewal management, or customer lifecycle workflows.\nPrior exposure to AI SDR, conversation intelligence, forecasting, proposal generation, or account planning tools.\nThis Role Is a Strong Fit If\nYou like ambiguous, high-impact problems and can turn them into a practical sequence of work.\nYou are comfortable challenging assumptions, but you do it constructively and with the goal of creating a better answer.\nYou care about adoption as much as architecture.\nYou can move between executive-level business context and detailed workflow design without losing the thread.\nYou are energized by building the operating model for how AI becomes part of daily GTM execution.","description_format":"text","description_chars":8573,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Cybersecurity","Information Technology","IT Infrastructure"],"lifecycle":[{"event":"open","at":"2026-09-24T02:42:29Z"}],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":0,"expected_fill_days":41,"reasons":["conf:3","velocity","win:early"],"computed_at":"2026-09-24T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/thrive-managed-services-aiautomation-analyst","json_url":"https://alion.io/job/thrive-managed-services-aiautomation-analyst.json","meta":{"generated_at":"2026-09-24T16:06:12Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers"}}