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