Data / ML Engineer

Why this role 🎯

Our forecasting algorithm and time-series models are already built. What we need now is someone to make them run reliably—ingesting messy client data, maintaining our GCP/BigQuery infrastructure, and shipping outputs consistently as we scale from pilot to production. This role owns that.

Who you are

You're a Data/ML Engineer with strong software engineering instincts. You write clean Python, you're comfortable wrangling dataframes, and you know how to build pipelines that don't break at 2am.

Required

🐍 1–3 years of commercial experience in data engineering, ML engineering, or a similar role

🛠️ Strong Python skills with hands-on pandas/dataframe experience

🔁 Experience building and running data pipelines (ETL/ELT) with orchestration tools

☁️ Familiarity with cloud platforms (GCP preferred) and tools like BigQuery

🗂️ Comfortable working with messy, real-world data—CSV ingestion, schema validation, inconsistent formats

🚀 Able to deploy and monitor models and pipelines in production

📊 Can build and maintain dashboards to give the team and clients real-time visibility into model health and forecast outputs

🤖 You love AI tools and use them to build shit fast—Cursor, Claude, Copilot, whatever gets the job done

📣 Clear communicator who documents well and cares about reliability, not just shipping fast

💃 Excited to work in a small, fast-paced startup where you'll take ownership and jump in wherever needed

Desirable

🧠 Enough statistics/ML knowledge to retrain, tune, and troubleshoot models—not just wrap APIs

🏗️ Experience owning infrastructure: environments, dependencies, CI/CD, rollbacks

🐳 Familiarity with containerisation (Docker) or infrastructure-as-code

🍽️ Interest in food retail, fresh food, or supply chain domains

What the job involves

Data pipelines 🔁 — Build, maintain, and improve our ETL/ELT pipelines. Ingest messy client data (CSV exports, varying formats, schema mismatches) and transform it reliably for forecasting.

Production systems 🚀 — Deploy statistical/ML models and keep them running at scale. Monitor pipelines and models, set up alerting for failures and data quality issues.

Visibility 📊 — Build dashboards so the team and clients can see model health and forecast outputs in real time. Surface insights when performance drifts.

Infrastructure ☁️ — Own our GCP environments, BigQuery setup, dependency management, and release processes.

Code quality ✅ — Write well-structured, tested Python with proper version control practices (branching, PRs, code review).

Client onboarding 📦 — Own the end-to-end process from receiving a client's first data extract to delivering clean forecast outputs.

Collaboration 🤝 — Work closely with our Head of Data Science to run, maintain, and troubleshoot existing models. Document systems and runbooks clearly.

Team & reporting 👯

• You'll be the first dedicated Data/ML Engineer, working directly with the founder (who loves using AI to build stuff and has a background in UX and product) as their technical sidekick. You'll also collaborate closely with our Head of Data Science and the rest of the founding team. This is a high-impact role where you'll shape how we build and scale our technology, data infrastructure from day one.