Implementation & Solutions Lead

The opportunity

Join as the company’s first hire and work directly with both founders. You’ll own customer implementation and onboarding.

The company has 16 live customers. It is building an AI-native merchandising and supply chain planning platform for growing consumer brands in fashion, homeware and consumer products. These brands have outgrown spreadsheets and need better ways to plan inventory, purchasing, forecasting and cash flow. Their ERP and WMS systems handle transactions, and the platform adds the planning, modelling and reporting layer on top.

You’ll take what the founders currently do themselves and turn it into a repeatable, scalable process. You’ll understand each customer’s data and planning needs, build their models, configure the platform and help them get value from it.

The role

The role spans three areas, and the challenge is being strong in all of them.

1. Data modelling. Turn messy customer data into an accurate model of how the business works:

• Work out how products, variants, suppliers and POs relate to each other
• Define the underlying tables and entities
• Map customer spreadsheets into a structured model
• Spot missing, duplicated or inconsistent data
• Make sure the model reflects how the customer actually operates

You don’t need to be a database engineer, but you should naturally think in entities, relationships, structures and dependencies.

2. Planning logic. Work with forecasting, seasonality, safety stock, size/colour mix, landed costs, supplier purchasing, POs, inventory, cash flow, margin and planning assumptions. You should be comfortable questioning numbers, tracing how a calculation was produced and explaining the result clearly.

3. Customer implementation. Get customers live by:

• Running onboarding meetings
• Explaining the model
• Managing expectations and handling pushback
• Finding workarounds when the platform can’t do something
• Communicating product gaps to the founders
• Building trust throughout

You should be comfortable saying “we can’t do that today” and still offering a sensible next step.

AI is part of how you work

Customers increasingly use the platform through Claude via its MCP, and the team uses tools like Claude Code to build and configure solutions. You’ll direct AI to build, modify, analyse and troubleshoot, then check its output against the data and business logic. You should be comfortable:

• Using AI tools for real work every week
• Reading and editing code or configuration
• Reviewing AI output critically and checking calculations rather than trusting them blindly
• Learning new AI tools quickly
• Turning repeated work into prompts, templates, automations or skills

You don’t need to be a software engineer, but you do need to be technically curious and comfortable with systems.

What the work looks like

• Turn three spreadsheets of open POs into a PO header/line structure, link the lines to variants and load the data
• Change the model so stock can be projected by colour as well as style, using AI to implement the change
• Trace why a peak-season forecast looks too low, explain the cause plainly and recommend a fix
• Compare 26-week and 52-week forecasts against historical actuals and recommend one
• Explain an unsupported feature honestly, offer a workaround and flag the gap to the founders
• Turn a mapping you keep repeating into a template, automation or AI skill

What you’ll own

• Customer implementation: own onboarding from kick-off to go-live, working alongside the founders at first and then taking full ownership. Translate requirements into platform configurations, manage timelines and expectations, and make sure customers are using the platform after go-live.
• Data modelling & configuration: design customer data structures, and map, clean and validate data. Configure models using AI and internal tooling, resolve data inconsistencies, and make sure outputs tie back to the source data.
• Planning & analysis: work with forecasting, inventory, purchasing and supply chain logic. Sanity-check calculations and assumptions, analyse historical performance and backtests, understand how changes to assumptions affect outputs, and flag numbers that don’t make commercial sense.
• Process improvement: document what works and what doesn’t. Build reusable templates, mapping guides and checks, find opportunities to automate, keep reducing implementation time, and build the implementation playbook.
• Product feedback: spot recurring customer needs and pain points, turn them into clear product feedback, help the founders prioritise, and feed customer insight into the roadmap.

First 90 days

1. Run at least one onboarding end to end, with a founder reviewing rather than leading
2. Measure implementation time against the current baseline and start bringing it down
3. Have at least one reusable template, automation or AI workflow in active use
4. Make sure the customers you onboard are actively using the platform
5. Start contributing to a repeatable implementation playbook

Who we’re looking for

All three areas are essential. Being very strong in two but weak in the third is unlikely to work.

1. You think in data structures. You look past a spreadsheet’s columns to the entities and relationships underneath. For example, you know a PO has multiple lines and each line links to a product or variant. You’ve worked with structured data or systems such as databases, SQL, Airtable, Notion, low-code platforms, planning systems, ERP/WMS, ecommerce platforms or internal tools. You’ve designed or structured data, not just consumed reports.

2. You use AI tools for real work. You regularly use tools like Claude, ChatGPT, Claude Code, Cursor or Copilot. You can read and edit code or configuration. You know AI can be wrong, and you validate its output against the data, the logic and business reality.

3. You’re strong with customers. You can sit with founders, buyers, planners or operations teams and understand what they really need. You can:

• Explain complex things simply
• Present recommendations confidently
• Ask good questions and challenge assumptions constructively
• Handle difficult conversations calmly
• Manage expectations and build trust
• Say no without damaging the relationship

Also important

• Planning logic: you can understand and sanity-check rolling averages, seasonality, forecast accuracy, backtesting, size/colour mix, discontinued products, safety stock, POs, landed costs, margin and supplier cash flow. You can follow a number from source data to business output and spot when it doesn’t add up.
• Improving systems: you’d rather improve a process than repeat a manual task. You naturally ask “how do we make this easier next time?” and you’re happy to help build a playbook that doesn’t fully exist yet.

Nice to have: experience in merchandising, buying, merchandise planning, supply chain planning, ecommerce operations, inventory planning or retail analytics, or with Shopify, Amazon, ERP/WMS, 3PL data, Python, SQL, YAML or low-code/no-code tools. Knowledge of WSSI, OTB or intake planning is a particular plus.

Backgrounds that could fit

We want people who have worked with both systems/data and stakeholders, for example:

• Merchandise, Buying or Planning Analyst at a consumer brand
• Ecommerce or Operations Analyst working with Shopify, inventory or 3PL data
• Solutions Consultant or Implementation Specialist at a B2B SaaS company
• Business or Data Analyst who has built internal tools
• Systems Analyst
• Technically strong Operations Analyst
• Hands-on technical consultant
• Implementation professional with strong data skills

Your title matters less than real experience across data, systems, problem-solving and customers.

Probably not for you if:

• You’ve focused on relationships while others handled configuration and systems
• You’re a developer who hasn’t worked directly with customers
• You haven’t used AI tools for real work
• Your spreadsheet work has mainly been reporting rather than modelling
• You prefer following an established process to creating one
• You enjoy managing implementations but not the underlying data and logic
• You want the technical team to handle the build while you manage the relationship

Details

Location: South Africa, fully remote

Hours: 9am–5pm UK time · Full-time

Reports to the founders

Pay: competitive, based on experience

Why this role?

Join a small, AI-native company early and shape how implementation works as the customer base grows. You’ll work closely with the founders, customers, product and roadmap, and you’ll build the implementation function rather than inherit a fixed process. If you enjoy data, systems, problem-solving, AI and working with customers, this role brings them together.

If this sounds like you, hit Apply.