We are working with a leading digital media company that specialises in content across lifestyle, home, health, and finance, reaching over 150 million users monthly. This organisation is at the forefront of leveraging data and technology to create highly personalised user experiences across its diverse portfolio.
The Role
- Own the science behind recommendation engines that power personalized product feeds.
- Design, build, evaluate, and continuously improve models for user taste across various product attributes.
- Take recommendation problems from raw data to production models on an existing MLOps stack.
- Address cold-start challenges with a growing product catalog, producing high-quality recommendations.
- Collaborate with MLOps, engineering, and product teams to integrate models into live products.
What You'll Need
- Master's degree or higher in a quantitative field or equivalent practical experience.
- Strong data science fundamentals: statistics, experimental design, and evaluation methodology.
- Demonstrated ownership of the full A/B testing lifecycle, from design to decision-making.
- Experience designing, training, and deploying embedding models and vector retrieval for product similarity.
- Direct experience with cold-start or sparse-signal personalization.
- Strong Python skills with modern ML frameworks (PyTorch, TensorFlow, JAX) and standard scientific stack.
- Strong SQL experience for querying large datasets in a cloud data warehouse (BigQuery preferred).
- Experience deploying and serving models on a cloud ML platform (GCP Vertex AI preferred).
What's On Offer
- The opportunity to shape how millions of users discover products they love.
- A hands-on, full-cycle role with significant impact.
- Collaboration within a dynamic team at the cutting edge of digital personalisation.
- Flexible work environment with remote options.
Apply via Haystack today!