Data Scientist

We are looking for a machine learning engineer (recommendations) to design, build, and scale personalised recommendation systems that power discovery, ranking, and user engagement across our products. You will work at the intersection of machine learning, data engineering, and backend systems, taking models from research to production.

The core responsibilities for the job include the following:

Recommendation and ML:

• Design and develop recommendation systems including collaborative filtering (user-item, item-item), content-based and hybrid recommenders, and ranking and re-ranking models.
• Embedding-based retrieval (ANN, vector search).
• Train, evaluate, and iterate on models using offline metrics (NDCG, MAP, and recall@K) and online A/B experiments.
• Build pipelines for feature engineering, model training, inference, and retraining.

Production ML and Systems:

• Deploy ML models in production environments with low-latency constraints.
• Optimise inference for scale (caching, batching, approximate nearest neighbours).
• Build real-time and batch recommendation pipelines.
• Monitor model performance, data drift, and system health.

Data and Experimentation:

• Work with large-scale datasets (clicks, impressions, transactions).
• Define success metrics for recommendations (CTR, CVR, retention).
• Run and analyse A/B tests and iterate based on results.

Collaboration:

• Work closely with product, data, and backend teams to translate business problems into ML solutions.
• Contribute to ML best practices, documentation, and system design.

The core requirements for the job include the following:

Core ML:

• Strong understanding of recommendation algorithms, ranking and learning-to-rank, embeddings and similarity search.
• Experience with Python and ML libraries (PyTorch / TensorFlow / Scikit-learn).

Data and Systems:

• Strong SQL skills; experience with large datasets.
• Experience with feature stores, data pipelines, and batch/stream processing.
• Familiarity with vector databases / ANN libraries (FAISS, ScaNN, Elasticsearch/OpenSearch KNN, Milvus).

Production and Infra.

• Experience deploying models using REST/gRPC services.
• Familiarity with Docker, Kubernetes, or cloud platforms (AWS / GCP / Azure).
• Understanding of latency, throughput, and scalability trade-offs.

Good to Have:

• Experience with: search or feed ranking systems, hybrid retrieval (BM25 + embeddings), and real-time recommendations.
• Knowledge of Kafka / streaming systems and MLOps tools (MLflow, Airflow).
• Experience in e-commerce, ads, content platforms, or marketplaces.

Nice Behavioural Traits:

• Strong problem-solving and system-thinking mindset.
• Ability to balance model quality vs. production constraints.
• Comfortable owning models end-to-end.