Lead Data Engineer / Applied ML (Hands-on)

Lead Data Engineer / Applied ML (Hands-on) — Circular Economy

Lead an existing 2-person team · EU-Remote or Milan

The hook

Own the data & ML engine behind a real-world re-commerce platform. 70% building, 30% leading . Ship pipelines and models that set prices, manage risk and match catalog, and feel your work move the business, every week.

Picture your first 90 days

• Week 2: You’ve mapped the data flows and agreed on a crisp, outcome-based roadmap.
• Week 6: A reliable ELT path with data contracts and monitoring is live; noise down, signal up.
• Day 90: A production model (pricing/risk/catalog) is shipped with automated evals, drift alerts and a measurable lift.

What you’ll do (outcomes, not just tasks)

• Set the bar (Tech Lead): Own architecture, standards and costs, so the team moves fast and safely.
• Level up people (Lead 2 → 3): Mentor two engineers via reviews, 1:1s,and lightweight rituals that improve delivery.
• Data Engineering: Build/operate batch + streaming ELT; enforce data contracts, lineage, SLAs/SLOs to earn stakeholder trust.
• Applied ML: Train, validate and deploy models for pricing, risk and product matching ; wire up A/B tests, drift & performance alerts and feedback loops.
• NLP & enrichment: Practical NLP (text cleanup, entity/product resolution) to boost search, classification and recommendations.
• Own production: Ship, measure, iterate, because value only counts in prod.

Stack you’ll touch (familiarity > checklists)

Python, SQL · Pandas/Polars · Spark (nice) · scikit-learn, PyTorch/TensorFlow, spaCy · Warehouses/Lakes · Orchestration & streaming · Containers & CI/CD · Cloud (AWS/GCP/Azure ) · IaC (plus) · Plotly/Seaborn for exploration.

Signals you’re the one

• You’ve led small teams (or were the de-facto lead) and still love to code.
• You turn fuzzy problems into simple, reliable data/ML systems.
• You’ve shipped end-to-end : features → training → serving → monitoring.
• You care about quality, cost and reliability as much as accuracy.
• You write clearly and work async with Product & Backend without drama.

Nice to have

Fintech/marketplace/re-commerce exposure · Streaming & feature stores · Vector search/RAG · Privacy & governance (PII, access control, lineage).

How we work (why this feels good)

• Autonomy + ownership: You make trade-offs, not slide decks.
• Fast feedback: Tight loop from idea → impact.
• Learning by shipping: Experiments beat ceremony, every time.

Offer & setup

Competitive salary + meaningful equity · Milan (IT) hybrid or EU-remote · Well-funded, product-obsessed environment.

Not for you if…

You want research-only or people-management-only. This is hands-on leadership .

Eligibility

BSc/MSc in CS/Engineering/Math (or equivalent).

How to apply (micro-ask):

Send your CV + GitHub/LinkedIn and 2–3 lines on (1) a team you led and (2) a pipeline/model you shipped that moved a metric, because results matter.