AI Deployment Strategist

Description

The company builds NEXUS, a Large Tabular Model designed for the structured records behind enterprise decisions, and it is backed by leading investors.

Its customers include Fortune 100 firms, and it claims NEXUS can unlock trillions of dollars of value for businesses through better predictions.

The role links research to what happens in customers' businesses, helping large companies get real results from NEXUS.

You own the outcome of each NEXUS deployment. A Forward Deployed Engineer owns the build, and you own the reasoning, the scope, and the follow-on work.

Most of your week is spent at customer sites, working with their data and their teams. This is a hands-on customer role, not an analyst or sales seat.

The company hires for potential and direction more than years in a job.

Responsibilities

• Run discovery with business and data leaders to link decisions to prediction problems, check whether data exists, and rank use cases by value and feasibility.
• Write a use-case brief for each deployment covering the business question, prediction target, data sources, baseline, success KPI, and the customer-side owner.
• Set the value measure before building and confirm it afterward, converting model improvement into money, time, or risk in terms the sponsor's CFO will accept.
• Drive adoption by getting the model into daily workflows, training users, tracking usage, and removing whatever blocks them.
• Maintain a live roadmap for each account and pass qualified expansion to the Account Executive with the business case already written.
• Support your Forward Deployed Engineer by clearing access to data, stakeholders, and priorities so they can build.
• Bring patterns from customers back to Product, Research, and leadership, and turn what works into playbooks for the team.

Requirements

• Two to six years in a role judged on customer or client results, such as consulting, technical sales, customer-facing data science, product, or founding or early-stage operating work.
• Technical curiosity with a working base in Python or SQL notebooks. You can explain a prediction problem and read a backtest, and you want to go further. This will be tested.
• Clear and confident communication: you can organize a messy problem on a whiteboard, write a concise one-page brief, and lead discussions with senior people.
• Proof that you learn quickly, such as a domain or technology you picked up fast and something you built or shipped.
• Comfortable with travel, long customer days, and ambiguity.
• Humble and team-first, willing to take on unglamorous work when it moves an account forward.
• Depth in one industry the company sells into: energy, financial services, insurance, shipping, retail, or private equity.
• Prior exposure to applied machine learning, forecasting, or analytics products.
• A second language used in business.

Conditions

Travel is more than half of the working time, with at least 50% spent at customer sites.

Work is primarily on-site with customers, in their offices and within their data and organizations.

Benefits

• Competitive base salary plus equity in a Series A company valued at $1.4B.
• Medical, dental, and vision insurance.
• Company equipment and stipends for wellbeing, learning, and home office costs.
• A structured eight-week ramp covering NEXUS, prediction framing, and value modeling, before you take on your own accounts.
• A culture that values diversity of thought, humility, and a bias toward action.