[Hiring] AI Deployment Strategist @Fundamental

Role Description

An AI Deployment Strategist owns the outcome of a NEXUS deployment. You sit with the customer, find the decisions where a better prediction changes the P&L, turn them into use cases an engineer can build, define how value will be measured before the first model is trained, and make sure the finished model gets used. You are paired with a Forward Deployed Engineer: they own the build, you own the why, the what, and the what next.

This is not an analyst seat and not a sales seat. You will spend most of your week with customers, in their offices, inside their data and inside their organization. Expect to:

• Run a discovery workshop with a VP of Operations on Monday
• Read a backtest with the customer's data science lead on Tuesday
• Write the one-page brief that gets the next use case funded by Friday

You will travel more than half the time.

We hire for trajectory, not tenure. The people who do well here are hungry, articulate, technically curious, and comfortable being the face of a young company inside a Fortune 100.

What will you do:

• Run discovery with business and data leaders: map decision points to prediction problems, qualify data availability, and prioritize use cases by value and feasibility
• Write the use-case brief for every deployment: business question, prediction target, data sources, baseline, success KPI, and owner on the customer side
• Define value before the build and sign it off after: translate model lift into dollars, hours, or risk in language the sponsor's CFO accepts
• Drive adoption: get the model into the customer's workflow, train the users, track usage, and fix what blocks it
• Shape the next use cases: keep a live roadmap per account and hand qualified expansion to the Account Executive with the business case already written
• Work hand in hand with your Forward Deployed Engineer: you unblock data access, stakeholders, and priorities so they can build
• Carry customer patterns back to Product, Research, and leadership, and turn what worked into playbooks the team reuses
• Travel to customer sites 50% or more of the time

Qualifications

• 2 to 6 years in a role where you were judged on outcomes with a customer or client: consulting, technical sales, customer-facing data science, product, founding or early-startup operating roles
• Technically curious with a working floor: comfortable in a Python or SQL notebook, able to explain what a prediction problem is and read a backtest, and eager to go deeper
• Articulate and confident: you structure a messy problem on a whiteboard, write a crisp one-pager, and hold a room of senior people
• Fast learner with evidence: you have picked up a domain or a technology quickly before and can show what you built or shipped
• Hungry and mobile: travel, long customer days, and ambiguity energize you
• Humble and team-first: you take the unglamorous task when it moves the account

Nice to have

• Depth in one industry we sell into: energy, financial services, insurance, shipping, retail, or private equity
• Prior exposure to applied ML, forecasting, or analytics products
• A second language used in business

Benefits

• Competitive base plus equity in a Series A company at a $1.4B valuation
• Medical, dental, and vision insurance; company equipment; wellbeing, learning, and home office stipends
• A structured eight-week ramp on NEXUS, prediction framing, and value modeling, then your own accounts
• A mission-driven culture that values diversity of thought, humility, and bias toward action