Data Scientist

Role Overview

The Data Scientist develops, validates, and deploys advanced analytical models and AI solutions that drive measurable improvements across the Operations function. This role applies machine learning, statistical modelling, and AI techniques to solve complex operational challenges in areas such as demand forecasting, predictive maintenance, procurement analytics, logistics optimisation, and intelligent process automation.

Key Responsibilities

AI & Machine Learning Development

• Design, build, and validate machine learning and statistical models to address Operations use cases

• Develop predictive and prescriptive analytics solutions for demand forecasting, lead time prediction, anomaly detection, and supplier risk scoring

• Explore and prototype generative AI and NLP solutions for process automation and operational intelligence

• Ensure model robustness, fairness, explainability, and alignment with business objectives

Data Analysis & Insights

• Perform exploratory data analysis on structured and unstructured operational data to uncover patterns and insights

• Develop analytical frameworks and dashboards that enable Operations teams to make data-driven decisions

• Collaborate with Business Analysts to quantify the impact of digital transformation initiatives

MLOps & Productionisation

• Partner with Data Engineers to build robust data pipelines and feature stores for model training and inference

• Deploy models into production environments using MLOps best practices (monitoring, retraining, drift detection)

• Maintain model performance and implement continuous improvement cycles

• Document model methodologies, assumptions, and limitations for technical and business audiences

Collaboration & Knowledge Sharing

• Work closely with Product Managers to translate AI capabilities into product features and business value

• Educate Operations stakeholders on AI capabilities, limitations, and responsible use

• Contribute to internal communities of practice and stay current on emerging AI/ML research

Qualifications & Experience

• 3–7 years of experience in data science or applied machine learning roles

• Strong proficiency in Python (Pandas, Scikit-learn, PyTorch/TensorFlow) and SQL

• Experience with ML techniques including regression, classification, clustering, time series forecasting, and NLP

• Familiarity with cloud-based ML platforms (Azure ML, AWS SageMaker, GCP Vertex AI)

• Experience deploying models to production with MLOps tooling (MLflow, Kubeflow, or equivalent)

• Knowledge of Operations or Supply Chain domain is highly advantageous

• Strong communication skills — ability to present complex analytical findings to non-technical stakeholders

• Master's degree or equivalent in Data Science, Statistics, Computer Science, Engineering, or related field preferred