About the Role
This role sits at the intersection of machine learning, big data engineering, and data science. You will work with large-scale datasets to develop predictive models, recommendation solutions, and intelligent systems that support product performance, user engagement, and business decision-making. You will be involved throughout the ML lifecycle—from data exploration and feature engineering to model development, evaluation, deployment, and continuous optimisation.
Key Responsibilities
- Develop and productionise machine learning models for user behaviour, recommendations, prediction, classification, and other data-driven applications.
- Analyse large and complex datasets to identify patterns, trends, and opportunities for product and business improvement.
- Perform data exploration, feature engineering, model selection, training, validation, and performance evaluation.
- Build scalable data and ML solutions to process large volumes of structured and unstructured data.
- Collaborate with Data Engineers to prepare, transform, and optimise datasets for machine learning applications.
- Optimise models for accuracy, scalability, latency, and production performance.
- Stay up to date with developments in machine learning, big data, and MLOps and evaluate their applicability to the company's products.
Requirements
- Bachelor's degree or above in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related field.
- 2+ years of experience in Machine Learning, Data Science or ML Engineering.
- Strong programming skills in Python and experience with common ML/data science libraries such as scikit-learn, Pandas, NumPy, PyTorch, or TensorFlow.
- Strong understanding of machine learning algorithms, statistics, model evaluation, and feature engineering.
- Experience working with large-scale datasets and distributed data processing technologies such as Spark, PySpark, Flink, or similar.
- Experience building or deploying ML models in production environments.
- Strong analytical and problem-solving skills, with the ability to translate business problems into data and machine learning solutions.
Nice to Have
- Familiarity with cloud platforms such as AWS, Azure, or GCP is an advantage.
- Experience with recommendation systems, ranking models, personalisation, or user behaviour modelling.
- Experience in gaming, e-commerce, fintech, advertising, or other data-intensive industries.
- Familiarity with ML deployment technologies such as Docker, Kubernetes, MLflow, or Kubeflow.