About the role
Responsibilities:
- Design, develop, and deploy advanced ML models for supply chain demand forecasting, inventory optimisation, and operational efficiency.
- Develop predictive models and optimisation algorithms using Python and ML frameworks.
- Build scalable, production-ready ML pipelines with automated testing and deployment.
- Collaborate with Data Platform, Product, and Engineering teams for model integration.
- Perform feature engineering, model training, performance tuning, and monitoring.
- Maintain code quality and stay updated with advancements in ML and Deep Learning.
Requirements:
- 3+ years of industry experience with a Bachelor's degree or 2+ years with a Master's degree in Computer Science, Mathematics, Statistics, or a related field.
- 2+ years of hands-on experience in Machine Learning / Data Science.
- Experience in Supply Chain Forecasting, Time-Series Modelling, or Operations Optimisation.
- Strong proficiency in Python and ML libraries such as Scikit-learn, PyTorch, TensorFlow, or Keras.
- Experience deploying ML models into production.
- Strong understanding of statistical modelling, ML algorithms, and scalable systems.
Nice to Have:
- Experience with demand forecasting, inventory optimisation, or logistics analytics.
- Knowledge of Spark, PySpark, SQL, and distributed computing.
- Experience with cloud platforms (AWS, GCP, Azure).
- Familiarity with MLOps tools such as MLflow, Airflow, or Kubeflow.
- Experience with optimisation tools such as Gurobi, OR-Tools, or Pyomo.
- Required Skills: Python, Machine Learning, Supply Chain Forecasting, Time-Series Forecasting, Operations Optimisation, PySpark, Deep Learning, Statistical Modelling, SQL, MLOps.