Founding Data Scientist, Commercial Intelligence

Founding Data Scientist, Commercial Intelligence

The quant.

Not the kind who builds models for models' sake. The kind who walks onto a trading floor, or into a pricing team, or a supply chain operation, and sees the system underneath. The signals everyone else is ignoring. The decisions being made by gut that should be made by evidence. The value sitting in the data that nobody has extracted yet.

You have spent your career building models that make money. Now you want to build models that make a business intelligent.

Healf is Europe's fastest-growing company.

Number one on the FT1000, number one on the Sifted 100.

From £1m to over £100m in under three years, with a small, talent-dense team and an electric culture with day one founder intensity.

Now we're aiming for £1bn in the next three.

We curate the world's best wellbeing brands across The Four PillarsTM: EAT, MOVE, MIND, SLEEP. That's the first chapter.

The next chapter is harder and more interesting.

We are moving from one market to many, from e-commerce to a technology platform, and from curating wellbeing to defining it. We are a health company, so we think we should act like one.

At its fullest expression, Healf redefines what wellbeing means for tens of millions of people.

Why this role is Healf

Healf is a hundred-million-pound business where nearly every commercial decision is still made by hand.

A pricing analyst pulls a lever in a spreadsheet. An inventory planner places a purchase order based on a formula they wrote last quarter. A marketing manager bids on search terms without knowing the real margin on the product they are driving traffic to. A product goes out of stock because nobody connected the demand signal to the replenishment trigger in time.

These are not admin problems. They are trading problems.

Pricing is a trading problem: what is the right price for this product, in this market, at this moment, given competitor positioning, margin targets, inventory levels, and customer elasticity? Inventory is a trading problem: when do you buy, how much, and what does it cost you when you are wrong? Marketing spend is a trading problem: you are bidding in a real-time auction, and the quality of your intelligence determines whether you win or waste.

Right now, gifted people are doing this work manually. Computing when they should be modelling. Reacting when they should be predicting. Pulling levers when they should be designing systems.

Nobody else has this data. Half a million regular customers buying products to change how they feel, across four wellbeing categories, with twelve months of transaction history and competitor pricing scraped daily. The commercial intelligence you build here could not be built anywhere else.

What you will own

→ Pricing intelligence. Models that understand price elasticity by product, category, customer segment, and market. Where is there headroom? Where are you bleeding margin? Where are competitors mispriced? Not a one-off analysis. A living system that learns.

→ Demand and inventory forecasting. Predictive models that tell the supply chain team what is going to sell, when, and what needs to be ordered before it runs out. Healf stops going out of stock because the system saw the signal before the human could.

→ Marketing and acquisition intelligence. The link between ad spend and commercial outcome. Which channels, products, and segments generate actual margin, not just revenue?

→ The commercial model. A unified quantitative view of how pricing, inventory, purchasing, and marketing interact. You will be the first person at Healf who sees the whole system and can quantify it.

→ The bridge to automation. You work directly with the Founding Engineer, Commercial Intelligence to turn your models into production systems that act on their own. Your models are not reports. They are the brains inside machines that trade.

Why the opportunity is bigger than it looks

Every decision in Healf's commercial operation is connected. Price affects demand. Demand affects inventory. Inventory affects purchasing costs. Purchasing costs affect margin. Margin should affect price. It is a loop. And right now nobody sees the whole loop because every team is optimising its own piece in its own spreadsheet.

The person who models the whole system creates enormous value. Not once. Continuously. Because the models learn.

And it is about to get more interesting. Project Atlas is taking Healf from one market into many. The pricing dynamics in Germany are different from the UK. The competitive set is different. The supplier economics are different. The commercial models that one analyst can hold in their head for one market become impossible to hold manually across five. That is where your work becomes the thing that makes international scale possible.

In the short term, better models drive better margins and fewer stock-outs in the UK. In the medium term, they make multi-market expansion commercially viable at pace. In the long term, the commercial intelligence you build becomes a proprietary asset that no competitor can replicate, because nobody else has the data, the models, and the feedback loops running simultaneously.

What you will have built in a year

→ A pricing model in production that demonstrably improves margin in at least one category → A demand forecasting system that reduces out-of-stocks measurably → A clear, quantified map of where Healf is leaving money on the table and what to do about it → Models that are trusted enough that commercial teams change their decisions based on them → The foundation of an autonomous commercial system that gets smarter with every transaction

How fast? Your first useful model should be in the hands of the commercial team within weeks, not months. The full system takes longer. But the pattern is: build something small, prove it changes a decision, expand.

Why you're Healf

You think in models. Not in the academic sense. In the applied sense. You see a business process and you immediately start thinking about the data generating it, the latent variables, the decision function, and where the value is. You have the instinct to look at a pricing spreadsheet and know, within minutes, that there is a better way.

You have built models that made real commercial decisions. Pricing models, demand models, trading signals, risk models, recommendation systems. Something where the output was not a presentation but an action. You know the difference between a model that is interesting and a model that is useful, and you have a strong preference for useful.

You are commercially literate. You do not need someone to explain margin, contribution, price elasticity, or customer lifetime value. You think naturally about whether your model actually makes the business money. You might have come from quantitative finance, revenue management, marketplace analytics, or management science. You might have come from somewhere else entirely. What matters is that you think like a trader: signals, risk, timing, and edge.

You are fluent with modern AI and ML tooling. You use LLMs as part of your analytical workflow. You build and deploy models quickly using the tools that exist now, not the tools that existed three years ago. The AI transformation of data science is not something you are reading about. It is something you are living.

You are impatient with analysis that does not lead to action. Dashboards that nobody looks at. Reports that confirm what everyone already knew. Models that are technically elegant but commercially irrelevant. You have a visceral need to see your work change something.

You are early enough in your career that this would be a defining role, but experienced enough that you have built something real. Not ten or fifteen years of experience. Sharp instincts, serious quantitative skill, and the drive to build something you can point to and say: that changed how the business works.

Show us

→ A model you built that directly influenced a commercial decision. What was the decision, what did the model say, and what happened?

→ Evidence you have worked with messy, real-world commercial data. Not clean academic datasets. The kind where the schema is wrong, the labels are missing, and the signal is buried.

→ A pricing, demand, or trading problem you solved where you can explain the approach, the tradeoffs, and what you would do differently now.

→ Proof you can move fast. A project where speed mattered and you delivered something useful in days or weeks, not months.

→ Something that tells us you care about commerce, not just computation. Why does making a wellness company commercially intelligent feel like work worth doing?

The deal

Competitive base plus meaningful equity for the right person.

We ask a great deal of the people who work here. We expect full ownership and a genuine commitment to give this chapter everything you have.

In return, we will give you the same: everything we have, invested in your growth, your wellbeing, and the defining skills of the next decade.

We have built the fastest-growing company in Europe with a team small enough that every person in it shapes the outcome. That is still true today.

The next person we hire will change the trajectory of the company.

If the most important work of your career is ahead of you, this is the place to do it.

One question

Include your answer in your CV or cover letter attachment when you apply.

Healf sells 2,000 SKUs across four categories (EAT, MOVE, MIND, SLEEP) and is about to launch in Germany. You have twelve months of UK transaction data, competitor pricing scraped daily, and marketing spend by channel. What is the single highest-value commercial model you build first, and what decision does it change?

200 words. Plain English. No decks. Show us how you think.