Company Overview

See https://gigawatt.ai

About the role

Gigawatt is building the AI-native field service platform for utilities: a scheduler that runs continuously and targets 95%+ automated dispatch, a mobile app built for crews working with or without coverage, and exception queues that surface only the work that needs a human. The Configuration Analyst - Service turns a utility's work types, crews, territories and business rules into a live, validated configuration of the Gigawatt Service module — so the schedule the engine builds is one dispatchers and crews can trust.

You own each unit from requirements through validated sign-off, using an AI-accelerated workflow (our configuration “flywheel”) rather than doing it by hand. This is a hands-on configuration role with real analytical depth - it is not a software-engineering role, and not a pure business-analysis role; you understand the requirements and build them into the system.

What you'll do

Configure the Service module end to end: For each unit (ticket type, skill, crew, dispatch area, calendar, SLA, exception rule, close-out), run intake → analyze → configuration guide → configure in dependency order → validate against real crew-days → sign-off and promote. 

Translate the utility's work into the platform: map work categories and legacy order types to ticket types, durations, required skills, priorities and SLAs - across customer and meter work, planned maintenance and inspections, projects and new construction, and emergency restoration.

Make the optimization engine trusted from day one: Convert legacy territories, routing tables and cross-references into dispatch areas and constraints, and set KPI weights per operating company. Treat the legacy system’s output as reference only, explain every difference to the client’s ops team, and tune until they trust the engine.

Tune exception rules: Anything the scheduler cannot place lands in a named queue with a reason, and the share of tickets a dispatcher touches keeps shrinking.

Model the constraints the scheduling engine must respect: skill and certification grades, crew composition, territory and dispatch-area boundaries, appointment windows, site access, permits and keys, parts and equipment, and work rules such as hours of service.

Replace scripts with configured business needs: For each hand-coded legacy rule, ask what business need it serves and whether the form builder and rules engine cover it. Retire workarounds, escalate real gaps as product requirements, and rely on online validation for the rest.

Set one standard across operating companies: Establish one best-practice configuration and hold it. Differences must justify themselves and live as configuration (KPI weight, jurisdiction rule, calendar), never a fork. The first operating company sets the standard, later ones are deltas.

Lead the change to engine-driven service: Crews move from picking work and self-correcting to receiving an engine-built day. Be the day-to-day face of it.

Run the AI-assisted flywheel, partner with Product and engineering on fit-gap, validate schedules against dispatcher expectations before go-live, triage production issues through the promotion process, and be the client’s day-to-day point of contact and first line of defect intake.

Minimum qualifications:

• 7+ years in field service management - configuring, implementing or consulting on FSM software for enterprise customers.
• Hands-on FSM platform configuration - hands-on, declarative setup of IFS Field Service Management, ClickSoftware / Salesforce Field Service, Oracle Field Service, ServiceNow Field Service Management, SAP Field Service Management or similar: work types, resources and skills, territories, calendars, and scheduling policies or optimization rules.
• Scheduling and dispatch fluency - you understand how constraints, priorities, SLAs and travel shape an optimized schedule, and why dispatchers override one.
• Implementation delivery - you've led requirements workshops with operations and dispatch stakeholders and taken configuration through testing, UAT and go-live on at least one enterprise rollout.
• Legacy migration: you have mapped and migrated data from a legacy field service or work-management system, including parallel runs and cutover.
• Change leadership: you have helped field organizations adopt a new way of working, and you are comfortable with supervisors, crew leads and labor stakeholders.
• Analytical comfort - you read an operating procedure or work-order history and see the rules and data model underneath; comfortable with structured data, spreadsheets and working alongside AI tooling.

Nice to have

• Utility field operations: distribution and transmission crews, meter and service orders, storm restoration, and inspection programs.
• Migrating a customer off a legacy field service or work-management system.
• Mobile workforce rollouts: offline-capable field apps, digital forms and safety briefings.
• Integration exposure: asset management (e.g., Maximo), GIS (e.g., Esri ArcGIS), outage management and inventory systems.
• Exposure to AI-accelerated / human-in-the-loop configuration workflows.
• The instinct to build reusable components instead of one-offs.

What success looks like (first 90 days)

• Ramped on the Service module's configuration model and how the scheduling engine uses it.
• Ticket-type model reconciled and signed off, and the migration mapping underway.
• Release 1 setup data configured for the first districts and validated against real crew-days with client dispatchers.
• Exception rules tuned, with the share of tickets needing a dispatcher touch measured and trending down.
• Reusable configuration templates (ticket types, crew models, calendars) that the next operating company or utility can start from.

Location

• Based in the US (any time zone) and able to travel to client sites.

Why Gigawatt

You'll configure field service for a major U.S. utility as the grid build-out accelerates, with AI doing the heavy lifting so you focus on judgment - not a back-office admin seat maintaining someone else's setup. It's a lean, senior, AI-accelerated team building a dispatch desk that scales with the grid instead of with headcount.