Role Overview
This is a builder's role. As a Product Success Analyst, you will sit directly with the client's problem, understand their business context, and build custom AI-driven solutions to solve it, using large language models (LLMs) and modern AI tooling. The model is forward-deployed: you work at the customer's problem rather than behind the product. You will move fluidly between two modes, understanding the business (what the client actually needs, what will create value, what is feasible) and building the solution (configuring, prototyping and assembling AI-powered workflows that deliver it)
- Both matter equally: a solution nobody needs is as useless as a need nobody can build for. You will own engagements end-to-end across multiple modules and clients simultaneously, and work closely with our product and engineering teams to take what you build from prototype to production.
Key Responsibilities
- Understand the client's business problem: engage directly with customer stakeholders to uncover what they need, what is feasible, and where AI can create real value.
- Build custom solutions using AI and LLMs: design, prototype and assemble AI-powered workflows tailored to each client, using LLM tools, prompting, and light scripting to turn a business problem into a working solution.
- Own delivery end-to-end: drive engagements across FAi's modules from pilot through full-scale rollout, across different client types and geographies.
- Work hands-on with data: prepare, structure and validate client datasets so that solutions are accurate and deployment-ready.
- Partner with product and engineering teams: take prototypes to production, feed client and field learnings back into the product, and help shape the roadmap.
- Manage client relationships: act as the day-to-day point of contact, manage expectations, and maintain engagement momentum and quality.
- Support pre-sales and solutioning: build quick proofs of concept and pre-analysis to demonstrate what FAi can do for prospective clients. Requirements
- 1–4 years of experience in a solutions, delivery, deployment, consulting, analytics, or product role.
- Demonstrated ability to build solutions using AI / LLMs — you have used tools like ChatGPT, Claude or similar, along with prompting and light scripting (e.g. Python), to build something that solved a real problem. This is the core of the role.
- Strong business and problem understanding: the ability to sit with an ambiguous, loosely-defined client problem, work out what actually needs solving, and translate it into a solution.
- Working proficiency with data: comfortable with Excel/Google Sheets and SQL, and structuring and validating data for use in a solution.
- Customer or stakeholder ownership: experience engaging external stakeholders directly, through delivery, implementation, or client-facing work.
- Strong written and verbal communication, able to engage both clients and internal technical teams clearly.
- Ability to manage multiple engagements concurrently across different clients and modules, including occasional field visits.
Good to Have
- Hands-on experience building with LLM APIs, agents, or AI automation / workflow tools.
- Exposure to FMCG, retail, CPG, or field-force / go-to-market domains.
- Conceptual familiarity with AI/ML, computer vision, or coverage optimisation (understanding of concepts, not model development from scratch).
- Experience in a startup or high-growth environment with fluid, evolving responsibilities.
- A bachelor's degree in engineering, business, analytics, or a related field.
