Remote Environmental Data Analyst Jobs in Raleigh, NC

Strategic Analytics is a dynamic and growing team at Arch that drives innovation and transforms how the businessoperates. We build AI-first, agent-driven products that change how Arch underwrites, services, and learns from its book - combining frontier LLMs, multi-agent orchestration (MCP, A2A), retrieval-augmented generation, evaluation harnesses, and traditional ML.Our missionis broad: agentic automation, decision support, AI-driven insights, and the platform engineeringrequiredto make all ofitproduction-grade.

We have a strongtrack recordof success(productionalizingdozens ofhigh qualityGen AI products over the last 3 years) -weaim to continue scaling these efforts and are seeking an AVP AIEngineering tolead thearchitecture & developmentof true multi-agent systems within Strategic Analytics. Reporting to theSVP of AI & Automation, you will design andoperateorchestrations where agents communicate directly with one another - not just sequential, hand-off-driven workflows.After architecting themultiagentsystem,you willautomate complex decisions by usingData Science frameworks/processes.This will happen in thesystemyoucreateandproductionsolutionsmust workathighlevelsof accuracy.You willpartnerwithImplementation Engineering (IE)on theorchestrationentry points and any infrastructure-side connectors.

Key Responsibilities

• Designourmulti-agent orchestration patterns (master-orchestrator + specialized worker agents) using protocols such as MCP and A2A.

• This may be a blend of build & buy

• Lead end-to-end delivery of agentic underwriting and claims automations, from prototype throughtoproduction.

• Use precision/recall, calibration, confidence thresholds, error analysis, and business-impact measurement todeterminewhen automation is safe to deploy

• Design decision frameworks that combine LLMs, retrieval, traditional ML, business rules, and human review."

• Identifythe conditions where the automation should be trusted, reviewed, or discarded

• Partner with IE on orchestration entry-point design (e.g., Azure Function endpoints, master-agent gateways) so the AE/IE seam is clean and scalable.

• Leadoffshore engineers and team members on agentic patterns, prompt engineering, and reliability practices.

• Establish coding, evaluation, and observability standards for agentic systems within the AI & Automation Center of Excellence.

• Translate business intent into working agentic systems - not just systems that compile, but systems that deliver measurable business outcomes.

Required Skills / Experience

• 7+ years of software engineering and/or automation engineering experience.

• 3+ years of Data science experience

• 3+ years of people leadership experience

• Demonstratedexperience buildingproduction grademulti-agent or agentic systems (beyondPOCwork).

• Strongtrack recordofdeveloping supervised learning models (ML and/or GLMs)that have a financially measurable impact on the business

• Strong experience sourcing & evaluating vendors

• StrongPython experience

• Resilient problem solving - comfortable with ambiguous problems and capable of breaking them into shippable increments.

• Strong written and verbal communication; able tooperateacross IE, AE, DS, and business stakeholders.

• Demonstrated, hands-on production experience (not POC-only) with the current AI stack: RAG, MCP / A2A or equivalent agent-to-agent protocols, agentic orchestration frameworks, and the ability to articulate where each is the right tool. Comfort scanning for and trialing new tooling as the space evolves.

Desired Skills / Experience

• P&C insurance domain familiarity - underwriting, claims, or submission lifecycle.

• Experience with retrieval-augmented generation (RAG),evaluationharnesses, and structured-output patterns.

• Cloud experience in Azure (preferred for our stack) and/or AWS; familiaritywith private endpoints and enterprise-grade safeguards.

• Experience leading multidisciplinary teams (onshore + offshore) for technology delivery.

• Visibletrack recordof self-directed learning in the AI space - side projects, contributions to agentic frameworks, write-ups, conference talks, or other evidence that the candidate is investing personal time staying ahead of the curve.

Education

• College degree in Computer Science, Software Engineering, Data Analytics, or equivalent practical experience.