About EatClub
At EatClub, we believe restaurants and bars are the beating heart of every city's culture. Whether it's discovering a hidden gem, grabbing a late-night takeaway, or meeting friends for a drink, our mission is simple: help the hospitality industry thrive through smart, powerful tech.
Our platform helps over 2 million customers discover top restaurants and access real-time deals that save them up to 50% off the bill. We empower more than 4,000 venues to fill empty tables, increase foot traffic, and maximise revenue.
Recently ranked #11 on the 2025 Deloitte Tech Fast 50. Now is an exciting time to join our team. Initially co-founded by Marco Pierre White and leaders in the food-tech scene, we're now a 150+ person scaleup growing fast and making waves in the industry.
Why You'll Love Working With Us
- Join a small forecasting team at the moment when accuracy lift directly converts to addressable market
- Deeply focus on modelling, model accuracy, and rapid experimentation to drive business outcomes.
- Work on a genuine industry gamechanger that "closes the loop" between demand prediction and revenue generation
- Operate in an AI-first culture (Claude Code via Anthropic Enterprise, agentic and async workflows expected)
- Ship in days, not quarters. Real customers, real feedback every week.
We're building the next generation of EatClub's venue platform: machine-learning-powered revenue forecasting, analytics dashboards, and an intelligent Actions Feed that helps restaurants make smarter decisions every day.
We are looking for a Senior Data Scientist with strong ML engineering skills - someone equally comfortable doing the hard forecasting and statistical work as they are shipping production infrastructure.
A Day-in-a-Life of our Senior Data Scientist
You will spend your days deep in modelling work - feature engineering, model selection, and cluster analysis - to improve accuracy across our forecasting, affinity, and restaurant grouping models. You will collaborate closely with the Senior Machine Learning Engineer to define requirements for the feature store, model deployment pipelines, and experimentation environment. You'll leverage AI leverage like AutoML and LLMs to expedite model iteration and improve results.
There's ambiguity. There's speed. There's ownership.
You will work closely with the Senior ML Engineer to refine MLOps requirements, the feature store, and deployment pipelines - the infrastructure that turns your modelling work into something production-ready. With backend engineers, you will work on POS data pipelines and serving APIs. With the Product Manager, you will have a conversation: what to measure, what to ship, and what to defer. Occasionally, the BD lead will pull you into a session with real restaurant operators - the moments where you hear directly how the forecast lands, or where it misses, in the context of an actual service.
On any given week, you will:
- Run a model evaluation across hundreds of venues, decide which approach wins on which cohort, and ship the result
- Refine requirements for the feature store, model deployment pipelines, and the Databricks-based experimentation environment in partnership with the Machine Learning Engineer
- Utilise AI tools like AutoML to expedite model iteration and leverage LLMs for explainability and feature engineering
- Productionise a new modelling approach (e.g. TiDE-class neural forecasters) and validate compute footprint before deploy
- Land "why we missed" variance attribution so the product can explain its own predictions
- Push the team's AI-first workflow forward: agentic loops, async runs, humans on final review
- Work on hard modelling problems and review your work with the Machine Learning Engineer.
Type of projects you'll be working on at EatClub
- Demand forecasting, affinity modelling for recommendations, and restaurant grouping for thousands of venues
- Defining requirements for the refined experimentation environment and feature store
- Per-venue model selection: deciding which model architecture wins for which venue cohort and operationalising the choice
- Variance attribution and forecast explainability, including the LLM-and-vector-DB direction for "why did this prediction change"
- Hourly / intraday demand modelling on top of the daily forecast
- The Actions Feed intelligence layer: turning forecast deltas into ranked, executable recommendations
- Forecast confidence: quantile loss, P10 / P90 bands, calibrated per-day confidence scores
You have
- Exceptional communication skills, specifically for delivering presentations on recent model iterations.
- Strong Python and modern data and visualisation tooling fluency (pyspark /pandas / scikit-learn, plotly, pytest)
- Demand-forecasting depth as your primary strength: comfortable with LightGBM/XgBoost and gradient boosting for tabular forecasting, plus at least one of Prophet, AutoARIMA, AutoETS, or TiDE-class neural forecasters.
- Quantile loss, exogenous regressors, time-series cross-validation second nature.
- Statistical rigour: experiment design, hypothesis testing, and causal reasoning
- Production MLOps experience: shipped models end to end, with hands-on experience in workflow orchestration, experiment tracking, and model serving. Solid understanding of how to use Databricks (the tool of choice for experimentation) and define requirements for MLOps/deployment pipelines
- Software engineering rigour: type checking, unit tests, CI/CD, code review discipline
- Strong "bias to action" and shipping evidence (not RFCs, shipped systems)
- "AI-first" working style: Claude Code, agentic workflows, AI in your daily loop
It would be extra awesome if you also had
- LLM, RAG, or vector-DB experience (we have a real use case in variance attribution and the Conversational Venue Assistant)
- Familiarity working with various services in AWS
- Hospitality, retail, demand-forecasting, or marketplace ML domain experience
- "E-shaped generalist" breadth: data science + ML engineering + data engineering + analytics + software engineering
- Experience setting up an ML team or pairing with an existing DS without territorial dynamics
You are
- Defaulting to the shortest path to a measured result in production
- Comfortable working alongside an existing strong ML Engineer as a peer, not over them
- Direct, low-ego, willing to be wrong in public
- Curious about the actual problem (restaurant operators making better decisions) not just the modelling artefact
- Treating AI tools as leverage, not as a novelty
If you do a good job
Production forecast accuracy moves into the "very good" band. Operators trust the forecast enough to act on it. Models ship from notebook to production in days. The MLOps platform exists and other teams want to use it. Variance attribution is live and the Conversational Venue Assistant can answer "why did the forecast change today" with grounded reasoning. The team is genuinely deep on ML.
Maybe this role is not for you if
- You prefer research over shipping
- You're uncomfortable owning ambiguous problems end-to-end
- You're uncomfortable working alongside an existing strong ML Engineer as a peer
- You've never owned an end-to-end production ML stack
- You want to focus purely on modelling or purely on infrastructure - this role requires both
If you're curious about what we're building, you're welcome to explore EatClub ahead of your interview. First-time users who choose to give it a try can use the code "ECAPPLY5" for an optional $5 voucher to test the experience. This is entirely voluntary and has no impact on your application or interview process.
One last note: even if you feel you don't meet every criterion above, we encourage you to apply. Past work experience is not the only indicator of future success, and we are on the lookout for hungry talent who want to grow with us.
