hotsourced
Website:
hotsourced.io
Job details:
The opportunity
You’ll be the company’s first hire, working directly with both founders to take ownership of customer implementation and onboarding.
The company has 16 live customers and is building an AI-native merchandising and supply chain planning platform for growing consumer brands across fashion, homeware and consumer products.
These are businesses that have outgrown spreadsheets and need better ways to plan inventory, purchasing, forecasting and cash flow. Their ERP and WMS systems continue to handle transactions, while the platform provides the planning, modelling and reporting layer on top.
As the first implementation hire, you’ll take what the founders currently do themselves and turn it into a repeatable, scalable process.
You’ll work directly with customers, understand their data and planning requirements, build their models, configure the platform and help them get value from it.
The role
The role sits across data modelling, planning logic and customer implementation.
The hard part is being strong across all three.
1. Data modelling
You’ll take messy customer data and work out how the underlying business should be represented.
For example:
- Understanding how products, variants, suppliers and purchase orders relate to one another
- Deciding what the underlying tables and entities should be
- Mapping customer spreadsheets into a structured model
- Identifying missing, duplicated or inconsistent data
- Making sure the final model accurately reflects how the customer's business operates
You don't need to be a database engineer, but you should naturally think in terms of entities, relationships, structures and dependencies.
2. Planning logic
You’ll work with the commercial and operational logic that sits on top of the data.
This includes areas such as:
- Forecasting
- Seasonality
- Safety stock
- Size and colour mix
- Landed costs
- Supplier purchasing
- Purchase orders
- Inventory
- Cash flow
- Margin and planning assumptions
You'll need to be comfortable questioning a number when it doesn't look right, tracing how a calculation was produced and explaining the outcome clearly.
3. Customer implementation
You'll work directly with customers to understand their requirements, guide them through the model and get them successfully live.
That means:
- Running onboarding meetings
- Explaining how the model works
- Managing expectations
- Handling pushback
- Identifying workable solutions when the platform doesn't currently support something
- Communicating product gaps clearly to the founders
- Building trust with customers throughout implementation
You should be comfortable saying “we can't do that today” when necessary, while still finding a sensible workaround or next step.
AI is part of how you work
The company is AI-native.
Customers increasingly interact with the platform through Claude via its MCP, while the internal team uses tools such as Claude Code to build and configure solutions.
You won't be expected to write everything from scratch.
Instead, you'll use AI to accelerate your work — directing it to build, modify, analyse and troubleshoot solutions, then checking the output carefully against the underlying data and business logic.
You should be comfortable:
- Using AI tools for real work every week
- Reading and editing code or configuration
- Reviewing AI-generated output critically
- Checking calculations and data rather than blindly trusting the result
- Learning new AI tools quickly
- Turning repeated work into reusable prompts, templates, automations or skills
You don't need to be a software engineer.
You do need to be technically curious and comfortable working with systems.
What the work actually looks like
Some examples of the work you'll handle:
- A brand sends three spreadsheets of open purchase orders. You determine that the data needs a purchase-order header and purchase-order-line structure, link the lines to product variants and load the data into the platform.
- A customer wants stock projected by colour as well as style. You determine what needs to change in the underlying model and use AI tools to help implement the change.
- A buyer says the forecast looks too low for peak season. You trace the calculation, identify what is driving the result, explain it in plain English and recommend a solution.
- A customer wants to move from a 26-week to a 52-week forecast. You compare the approaches against historical actuals, assess where accuracy improves or deteriorates and make a recommendation.
- A customer asks for functionality the platform doesn't currently support. You explain this honestly, identify a practical workaround and communicate the product gap to the founders.
- You notice you've completed the same mapping several times. Instead of repeating the work, you turn it into a template, automation or AI skill so the next onboarding is faster.
What you'll own
Customer implementation
- Own customer onboarding from kick-off through to go-live
- Work alongside the founders initially, gradually taking full ownership
- Understand customer requirements and translate them into workable platform configurations
- Manage implementation timelines and customer expectations
- Ensure customers are successfully using the platform after go-live
Data modelling & configuration
- Design customer data structures within the platform
- Map, clean and validate customer data
- Configure models using AI and the company's tooling
- Investigate data inconsistencies and resolve issues
- Ensure platform outputs tie back to source data
Planning & analysis
- Work with forecasting, inventory, purchasing and supply-chain logic
- Sanity-check calculations and assumptions
- Analyse historical performance and backtesting
- Understand how changes to assumptions affect outputs
- Identify when numbers don't make commercial sense
Process improvement
- Document what works and what doesn't
- Build reusable templates, mapping guides and checks
- Identify opportunities for automation
- Continuously reduce implementation time and effort
- Build the company's implementation playbook as the customer base grows
Product feedback
- Identify recurring customer requirements and pain points
- Translate implementation issues into clear product feedback
- Help founders prioritise product improvements
- Feed customer insight directly into the product roadmap
What success looks like in your first 90 days
By 90 days:
- You've successfully run at least one customer onboarding end to end, with a founder reviewing rather than leading.
- Implementation time is being measured against the current baseline and beginning to decrease.
- At least one reusable template, automation or AI workflow is actively being used.
- Customers you've onboarded are actively using the platform.
- You have started contributing to a repeatable implementation playbook.
Who we're looking for
All three areas are essential
Being exceptionally strong in two but weak in the third is unlikely to work in this role.
1. You think in data structures
Given a spreadsheet, you can look beyond the columns and understand the underlying business entities and relationships.
For example, you understand that:
A purchase order has multiple lines, and each line relates to a particular product or product variant.
You've worked with structured data or systems before — perhaps through:
- Databases
- SQL
- Airtable
- Notion
- Low-code platforms
- Planning systems
- ERP/WMS systems
- Ecommerce platforms
- Internal business tools
Your spreadsheet experience should go beyond reporting. You should have experience designing or structuring data, not simply consuming reports.
2. You use AI tools for real work
You use AI tools regularly to solve real problems and accelerate your work.
You may have experience with tools such as:
- Claude
- ChatGPT
- Claude Code
- Cursor
- Copilot
- Other AI-assisted development or productivity tools
You're comfortable reading and editing code or configuration, even if you're not a software engineer.
More importantly, you understand that AI can be wrong. You validate its output against data, logic and business reality.
3. You're strong with customers
You can sit with a founder, buyer, planner or operations team and understand what they actually need.
You can:
- Explain complex information simply
- Present a recommendation confidently
- Ask good questions
- Challenge assumptions constructively
- Handle difficult conversations calmly
- Manage expectations
- Build credibility and trust
- Say no when necessary without damaging the relationship
Also important
You can reason about planning logic
You don't need to be a supply-chain mathematician, but you should be comfortable understanding and sanity-checking concepts such as:
- Rolling averages
- Seasonality
- Forecast accuracy
- Backtesting
- Size and colour mix
- Discontinued products
- Safety stock
- Purchase orders
- Landed costs
- Margin
- Supplier cash flow
You should be able to follow a number from its underlying data through to the final business output and identify when something doesn't add up.
You like improving systems
You'd rather improve a process than repeatedly perform the same manual task.
You naturally ask:
“How do we make this easier next time?”
You're comfortable joining an environment where the playbook doesn't fully exist yet and helping build it.
Nice to have
Experience in any of the following would be advantageous:
- Merchandising
- Buying
- Merchandise planning
- Supply-chain planning
- Ecommerce operations
- Inventory planning
- Retail analytics
- Shopify
- Amazon
- ERP or WMS systems
- 3PL data
- Python
- SQL
- YAML
- Low-code/no-code tools
Experience with concepts such as WSSI, OTB or intake planning would be particularly useful.
Backgrounds that could fit
We're particularly interested in people who have worked across both systems/data and stakeholders.
Potential backgrounds include:
- 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 or systems
- Systems Analyst
- Operations Analyst with strong technical capability
- Consultant or analyst with a hands-on technical streak
- Implementation professional with strong data and analytical skills
Your job title matters less than whether you've actually worked across data, systems, problem-solving and customers.
Probably not for you if...
- Your strength is relationship management, while configuration and systems work have always been handled by someone else.
- You're a developer or engineer who hasn't worked directly with customers.
- You haven't yet used AI tools for real work.
- Your spreadsheet experience has primarily been reporting rather than structuring or modelling data.
- You prefer following an established process rather than creating and improving one.
- You enjoy implementation management but don't want to get into the underlying data and logic.
- You want a role where the technical team handles the build while you manage the customer relationship.
Work schedule & benefits
Location: India Fully Remote
Hours: 9:00am–5:00pm UK time
Employment: Full-time
Reporting to: Founders
Compensation: Competitive, based on experience
Why this role?
This is an opportunity to join a small, AI-native company at an early stage and have a meaningful influence on how implementation works as the customer base grows.
You'll have direct exposure to the founders, customers, product and roadmap — and the opportunity to build the implementation function rather than inherit a fixed process.
Click on Apply to know more.
