Deployment Strategist
About the role
Laurence builds quantitative models for autonomous advertising; deployment strategist role focuses on onboarding, managing accounts, and translating business needs into product improvements to scale customer impact.
Responsibilities
- Get brands off to a great start by understanding their economics, agreeing on success metrics, and leading onboarding with clear responsibilities and deadlines.
- Understand performance by investigating changes, challenging assumptions, and recommending actions with rationale.
- Turn customer problems into product improvements by providing engineers with evidence and business context; engineers own building and shipping changes.
- Make the next account easier by building reporting, onboarding, and escalation processes for scale.
- Earn the next commitment by addressing renewal risks and identifying opportunities for expanded use.
Qualifications
- 1–4 years in account management, customer success, strategy consulting, implementation, or startup operations
- Experience owning a customer relationship or deliverable and delivering measurable results
- Ability to analyze messy data in Excel/Google Sheets; explain impact on revenue, spend, and margins on profit
- Experience presenting recommendations to customers or senior stakeholders and handling pushback
- Ability to learn new software quickly and provide engineers with useful evidence
- Able to manage competing deadlines and cross-team coordination
- Experience with technical products, Amazon advertising, performance marketing, or ecommerce is useful
Skills
Benefits
- Offers equity.
About Laurence
Autonomous performance marketing
Advertising today is like trading in the 1980s pits: archaic, manual, and iterative guesswork. Humans update bids and keywords using noisy, delayed data with fixed rules that both break at scale and ignore stochasticity. We've built quantitative systems that advertise the way modern hedge-funds trade markets: continuous decision-making under stochastic outcomes and explicit profit constraints. We use customers' existing ad copy and use reinforcement learning and train our own LLMs to run ads on autopilot, harvesting profits when we’re confident and borrowing signals from similar keywords when data is sparse. Starting with brands on Amazon, we are automating tens of millions in live ad spend, increasing gross sales while decreasing advertising cost of sales by 40%.