Quick overview
Job description
About us
Hi there 👋 We're Wild. We set out to remove single-use plastic from the bathroom and ended up building one of the fastest-growing personal care brands in Europe. Our refillable, natural deodorant is now in millions of hands, our cases are collected like accessories, and our collaborations - from Disney to seasonal scent drops - keep the brand fresh, playful and impossible to ignore. Now part of the Unilever family, we have the backing to go global while keeping the pace, ambition and mischief of a start-up.
🚀The Role
We’re looking for a hands-on, commercially minded Data & AI Lead to run our data stack and lead the team that builds on it.
Wild is moving to an AI-driven operating model. We want agents working on our data to prepare or do much of the routine work - trading reviews, month-end, reconciliation, supplier checks - so our people spend their time on judgement and decisions. That only works if our data layer provides the context for these agents to be effective.
You’ll lead our in-house data team of analytics engineers and AI and automation engineers, work with our offshore engineering team in India, and own the full stack, including tooling decisions. The foundations are in place (Fivetran, Snowflake, dbt Cloud, ThoughtSpot, GCP). Your job is to take us from a data function that produces answers to one that builds systems, with AI as the route to fully self-service data.
This is a Lead role reporting into our Chief Finance & Operations Officer, based in London (hybrid). AI is changing how Wild runs, and this role will grow with it. We want someone who moves at the same pace.
⚡️What you'll do
🏗️ Data tooling and pipeline
Be accountable for our data stack (Fivetran, Snowflake, dbt Cloud, ThoughtSpot, GCP): reliable, secure and cost effective, with the team running it day to day
Make the tooling decisions: choose the right tools, manage contracts and vendors, and keep platform costs in check
Set the standards for CI/CD, data contracts and testing, and make sure the team follows them: checks on every change, separate environments, and validations so problems are caught in the pipeline, not in the board pack
Keep up with new features across the stack and make sure the team brings in the ones that matter
🧠 AI operating model
Be the technical owner of how Wild moves to an AI-driven operating model: architecture, sequencing and the build roadmap, agreed with our operations and finance leads
Drive our data and AI strategy, and run AI as a portfolio, ranked by value to margin, cash, time and risk
🤖 Skills and agents
Centrally own and deploy the Wild Skills library: version-controlled in Git, reviewed by PR, released through CI/CD, with a named owner for each skill
Own skill and agent development and evaluation: golden sets of known questions and answers, regression tests on every change to a prompt, skill, model or data definition, checks on tool calls and outputs before release, and monitoring in production so we know when answers drift
Be our expert on agents and automation, and help teams in Finance, Commercial, Supply Chain and Marketing scope, build and ship their own, with the right patterns, data access and guardrails
📊 How Wild uses data
Lead the evolution of how we consume and interact with data, through a mix of self-serve, data artifacts and dashboarding, using the right one for each job
Own the semantic layer as the single place we define our metrics, used by ThoughtSpot, Claude and every agent we build
Own the accuracy and context of all data surfaced through AI. As data becomes accessible to everyone in a powerful way, every answer needs to be right and come with the correct definitions, caveats, freshness and source
Own how AI connects to our data (MCP, role-based access, row-level security, audit logging), with our data in the best possible shape for access, privacy and responsible use
📈 Commercial and forecasting
Work closely with Finance on our P&L and understand what moves margin in a DTC and retail business
Lead how we model customer value: LTV, CAC and CAC payback by channel, product and cohort, using predictive cohort modelling and machine learning to forecast retention, repurchase and subscription churn from early signals
Help Finance build better forecasting models for demand, revenue and cash, with predictive inputs from the data team (cohort curves, repeat rate, subscriber retention, retail rate of sale), tested against actuals so we know how accurate they are and where they break
👥 Leadership
Be Wild’s thought leader on AI and automation as we move to a new way of working. Know what’s changing in tools, models and practice, decide what matters for Wild, and bring the business with you
Teach the business: run demos, sessions and playbooks so Finance, Commercial, Supply Chain, Marketing and the leadership team know what AI can do, how to use it safely, and think bigger about what’s possible
Change how teams work, not just the tools they use: how we scope, build, test and ship, and what we stop doing by hand
Be our senior voice on AI and automation, with the leadership team and with Unilever’s data and AI teams
Lead and develop a team with different skills, including analytics engineers, AI and automation engineers and offshore colleagues
Set clear priorities, use good judgement on what comes first, and make sure projects get delivered
Lead by example: review code, approve PRs and coach the team to do their best work
What we're looking for
You’re an engineer who leads. You can write the dbt model, review the agent code and debug the pipeline yourself, and you’re as comfortable talking contribution margin in a trading review as orchestration in an architecture review.
Deep, hands-on experience with Snowflake and dbt, ideally with CI/CD for data, and good experience with GCP or other Cloud infrastructure
A track record of taking AI from pilot into production: LLM-based agents or automation on company data, with evals, monitoring and governance. Show us what you’ve shipped
Evidence of a steep trajectory: promotions, scope or impact that came early
Experience designing data architecture, choosing tooling and managing vendors and contracts
Strong knowledge of data governance and semantic layers, including access, privacy and keeping AI answers accurate and in context
Experience leading or mentoring engineers, with a track record of getting projects delivered
A commercial mindset, with a clear understanding of margin and unit economics (LTV, CAC, payback) in a DTC and retail business
You use AI every day and have changed how your team works because of it. You have a clear point of view on where AI and automation are heading, and a record of bringing others with you (training, playbooks, talks or internal adoption)
The ability to work at pace and juggle a lot at once
⚡️What you’ll get from us:
25 days holidays + bank holidays + 9 extra remote working days
Hybrid working, 3 days a week in our shiny new London office
Enhanced maternity, paternity & parental leave
4% Company Pension
Mental well-being support through Spill
Private healthcare through Vitality
Weekly early finishes and social events
Annual L&D budget
Free breakfast
Free & discounted Wild products
2 x team volunteering days and 2 x personal volunteering days
Access to cycle to work & cycle saver scheme
Ready to become a Wild thing?!
At Wild, our mission to eliminate single-use culture needs fresh thinking, varied perspectives and bold ideas. We are committed to a just, equitable, diverse and inclusive workplace where everyone feels valued, respected and empowered to bring their authentic selves to work.
We recognise that diversity goes beyond visible differences such as race, gender, age and ethnicity. It includes sexual orientation, neurodiversity, physical ability, socioeconomic background, religion, thought and experience. We encourage people from all backgrounds to apply.
If we can make any adjustments to help you perform your best in the process, please let us know! You can read our full JEDI Commitments on our careers page.