Principal Consultant

The Business Consulting Practice turns enterprise AI into realized value: fixed-price Value Discovery, fixed-price initiatives from the resulting roadmap, and change management to ensure adoption and value realization. The AI Supply Chain pod owns this arc for the supply chain and logistics vector; the Supply Chain Transformation Lead owns the vector's P&L and pipeline. You are the pod's senior consultant embedded with the client.

This is a client-site role. Your primary objective is to work shoulder-to-shoulder with client supply chain teams — planners, buyers, logistics and fulfillment operators, and their leadership — to design, land, and scale AI and agentic initiatives in their operation: digital workers in planning and replenishment workflows, agent-assisted exception handling in fulfillment, LLM-based decision support for sourcing and allocation. You own these initiatives from diagnostic through production adoption. The value is realized on the client's floor, not in a deck, and you are the person standing on that floor. Houston is the strongly preferred base; you work at the client site by default, with the pod and GD engineering behind you.

Pre-sales involvement is limited and occasional — the Lead carries the pipeline. Your non-billable time goes mostly to codifying what you learn in delivery into reusable practice IP.

What You'll Own

On-site diagnostic and initiative shaping

• Run the as-is diagnostic inside the client's operation: reconstruct supply chain flows from their data (ERP, OMS, WMS, planning systems), walk the actual process with the people who run it, and reconcile what stakeholders say against what the data shows
• Assess current state against the supply chain analytics maturity model (L1 data-not-enabled → L5 process automation) across the client's sourcing, replenishment, allocation, fulfillment, and operations functions
• Identify and size the AI/agentic opportunities — where an agent acts autonomously, where it drafts for a human, where it stays out — and build the numbers (flow analysis, activity-based costing, scenario modeling) so each initiative carries a dollar figure the client's finance team will accept
• Co-design target workflows with the client's supply chain leadership and secure working-level buy-in from the teams whose jobs change
• Lead AI/agentic initiatives end-to-end within the client engagement: translate the design into requirements the GD engineering pod builds against, and keep the build honest to the business case
• Own the human-in-the-loop design: escalation paths, guardrails, agent governance, and the planner/buyer interaction model that determine whether a digital worker is trusted or bypassed
• Own KPI baselines and the value-realization measurement framework; track leading indicators, surface course corrections early, and report measured value to the client sponsor and the Lead

Adoption and change management

• Run change management execution for the roles whose workflows the AI systems transform: enablement, adoption routines, floor-level coaching through the transition
• Build the client-side operating rhythm — governance, exception review, model/agent performance review — that keeps initiatives performing after go-live
• Be the trusted daily face of Grid Dynamics on the account; surface expansion opportunities you see on the ground to the Lead

Practice contribution (the minor share of your time)

• Codify delivery learnings into reusable IP: diagnostic accelerators, agent design patterns, adoption playbooks, benchmark data
• Occasionally support the Lead in pre-sales — a reference conversation, a workshop appearance, a sanity check on an estimate — as delivery commitments allow

Domain Scope

You must be able to run analysis and shape solutions — with engineering counterparts owning build — across:

• Demand sensing and forecasting — hierarchical, cold-start, slow-mover, and promotion-aware forecasting; downstream integration into assortment, workforce, and reordering decisions
• Inventory flow control — safety stock optimization, automated replenishment, strategic allocation, rebalancing automation; coordinated supply-demand optimization
• Network and fulfillment — topology and flowpath optimization, order sourcing optimization (shipping cost, split reduction), carrier optimization, promise services (EDD/ATP)
• Supply risk and resilience — supplier performance and revenue-at-risk analytics, contract risk scanning, sourcing option optimization, disruption prevention
• Warehouse, intralogistics, and store operations — layout simulation and optimization, computer-vision packaging/sortation/planogram verification, workforce shift optimization
• Supply chain control tower — the integrating layer: global inventory visibility, process automation agents, and the platform architecture (data mesh, AI/agentic platform) underneath it

Depth in two or three of these vectors with working fluency across the rest beats shallow coverage of all six. Across all of them, the through-line for this role is the agentic layer: where and how digital workers enter these processes, and what it takes for the client's teams to adopt them.

What We're Looking For

• 7–10 years in supply chain operations, supply chain consulting, or supply chain analytics, with at least 2 engagements or programs where your work directly drove a quantified P&L outcome
• Hands-on experience putting AI into supply chain workflows — not just models in notebooks: LLM-based systems, context engineering (how enterprise data, policies, and process state are structured and fed to agents), and agentic process augmentation deployed to real planner, buyer, or operations teams
• Hands-on analytical toolkit — this is a doing role: SQL and Python sufficient to reconstruct flows from raw ERP/OMS/WMS data without an analyst intermediary; flow and queueing metrics; cost modeling; scenario analysis
• Working command of the model classes behind the domain: demand forecasting (GBDT, time-series, deep learning), mathematical optimization (LP/MILP for allocation, routing, replenishment), and where each earns its keep — you scope and challenge solutions, and you can build the prototype that proves the point
• Client-embedded consulting craft: you can hold a long-running daily presence inside a client organization — building trust with operators and skeptics, running structured interviews and workshops, and producing executive-ready synthesis — without going native or losing the outcome thread
• Change management grounding (Prosci or equivalent) and evidence of driving adoption on the floor, not just designing it
• Familiarity with the platform landscape: Google Vertex AI, Dataiku, AWS Supply Chain, IBM Sterling OMS, and modern data/agentic architectures
• Fixed-price discipline: you can scope your workstream, hit the date, and protect the margin
• Based in or willing to relocate to the Houston metro (elsewhere in Texas considered); on-site presence at the client is the default working mode, with travel as engagements require

Differentiators

• Prior operating role in planning, logistics, procurement, or fulfillment before consulting — you've run the process you now redesign
• Experience with foodservice, distribution, CPG, retail, or energy supply chains — the industries concentrated in the Houston/Gulf Coast corridor
• Discrete-event simulation or digital-twin modeling experience
• Track record converting analysis into technology delivery scope — your diagnostics have become someone's build backlog