Our Data team sits at the core of that growth. We build the ML systems, data pipelines, and platform capabilities that drive intelligent decisions across the business — from fraud and risk to personalisation and LTV. If you want your models to matter, this is the place.
The Product Growth team this role attaches to is similarly oriented: We build technical solutions to enable more and better marketplace activity for our customers.
The Problem You'll Own
Recommendations are one of the highest-leverage surfaces on our marketplace. We already have a recommendation system in production — and now we want someone to take us to the next level.
This isn't a "maintain and monitor" role. We're looking for an engineer who will challenge our current approach, prototype new ideas, run experiments, and ship models that measurably move engagement and revenue. You'll own the full recommendation ML lifecycle — from understanding user behaviour signals to deploying and iterating on production-grade models — and work closely with product, engineering, and data platform teams to make it happen.
Analyse user behaviour data (purchase history, browsing patterns, game genre preferences, session signals) to identify high-value personalisation features
Design, train, and iterate on recommendation models — from collaborative filtering and matrix factorisation to sequence-based and embedding-based approaches
Build and maintain end-to-end training and serving pipelines in collaboration with data and backend engineers
Define and track evaluation metrics — offline (precision@k, NDCG, coverage) and online (CTR, conversion, revenue per session) — tied directly to business KPIs
Run rigorous A/B tests to benchmark new approaches against the current internal baseline
Own monitoring and observability of deployed models: data drift, prediction distribution shifts, latency, degradation
Contribute reusable user and item features to our feature store
Hands-on experience designing and shipping recommender systems — collaborative filtering, content-based, hybrid, or sequence-based. You've gone beyond tutorials and built things that shipped and improved real metrics.
End-to-end ML ownership — you've taken models from raw data through feature engineering, training, evaluation, API wrapping, deployment, and production monitoring. You don't hand off at the notebook stage.
Strong Python and MLOps fluency — extensive Python for model development, plus experience with MLOps tooling (MLflow or similar) for experiment tracking, model versioning, and lifecycle management.
Experience with real-time or streaming inference (Kafka, Flink) for session-based recommendations
Familiarity with Databricks and/or Apache Spark for large-scale data processing
Production experience with feature stores (Databricks Feature Store, Hopsworks, Feast, or similar)
Knowledge of two-tower / embedding-based retrieval at scale
Familiarity with bandit algorithms or reinforcement learning for online recommendation optimisation
Strong business communication skills — you can translate model results and experimental findings into clear, actionable language for product and commercial stakeholders.
Parexel
Nagarro
Ad Hoc LLC
Clarifai
Parexel
Eneba
Eneba
Eneba