DATA SCIENTIST

Introduction

Function / Division Consumer CVM and Loyalty

Reporting Line Senior Manager

Duties & Responsibilities

• Build and enhance personalised recommendation models for the personalised inbound product platform. • Develop models for customer propensity, product selection, ranking, pricing and affordability optimisation. • Evolve existing scoring approaches towards adaptive, data-driven ML and AI decisioning. • Develop customer-level features using usage, spend, recharge, purchase, engagement and response behaviour. • Develop optimisation approaches that balance customer relevance, affordability, conversion and commercial value. • Build, train, validate and optimise predictive and recommendation models using appropriate statistical and machine-learning techniques. • Develop learning-to-rank, experimentation, uplift and adaptive-learning approaches where appropriate. • Design robust control groups, champion/challenger tests and experimentation frameworks. • Establish appropriate measures of model performance, including predictive accuracy, lift, calibration and incremental commercial impact. • Build model explainability and interpretability into recommendation decisioning. • Monitor model performance, feature drift and model degradation over time. • Define modelling standards, review technical approaches and provide design assurance for work delivered by other data scientists. • Select fit-for-purpose algorithms and establish robust champion/challenger, back-testing and model validation approaches.

Data Analysis &

Insight Generation • Perform advanced analysis (e.g., cluster profiling, cohort analysis, penetration/lift analysis) at scale using Spark/Databricks on large datasets (10M+ rows). • Generate business-friendly insights and recommendations from model outputs for stakeholders. • Query and manipulate data using SQL (Hive/Trino) and Python (pandas, PySpark). • Lead complex analytical investigations across multiple data domains and convert findings into clear commercial decisions and measurable actions. • Establish reusable analytical frameworks, curated features and quality checks that improve consistency and speed across the team

MLOps &

Productionisation • Own and lead the transition of models from development into production, including monitoring, regression testing and performance evidence. • Apply and enforce MLOps best practices (version control, experiment tracking, CI/CD, model monitoring) in partnership with the AI Ops function.

• Track and report on the performance and drift of models deployed in production. • MLOps capability is mandatory: independently implement reproducible training pipelines, experiment tracking, model registration, automated testing and controlled deployment workflows. • Define production-readiness criteria covering data quality, model validation, explainability, security, documentation, rollback and operational support. • Establish monitoring for data drift, concept drift, model performance, service reliability and business outcomes, with clear alerting and remediation processes. • Drive model lifecycle governance, technical documentation and audit evidence in partnership with AI Ops, data engineering, architecture and risk stakeholders.

Collaboration &

Capability Building • Support upskilling initiatives (MLOps, coding standards, Agile/Scrum for data science) and mentor graduates/interns. • Provide technical leadership, coaching and structured peer review to data scientists, graduates and cross-functional delivery teams. • Lead solution design across data science, data engineering, MLOps, BI and architecture, resolving dependencies and technical risks early. • Improve team capability through

Desired Experience & Qualification

Minimum 3-year tertiary degree in a STEM field: Computer Science, Engineering, Mathematics, Statistics, Data Science or a related quantitative discipline.

• Honours or Master's degree in Data Science, Statistics or a related field advantageous.

• Fluent in English.

Experience

• 5-10 years' experience in data science / advanced analytics, including ownership of complex solutions from business problem framing through production deployment and value realisation.

• Experience working with large-scale distributed data processing (Spark/Databricks) is highly advantageous.

• Demonstrated hands-on MLOps experience and a track record of deploying and operating machine learning models in production are mandatory.

• Experience with model governance, explainability, risk controls, data quality and production monitoring in an enterprise environment.

• Strong experience engaging senior business stakeholders, defining measurable success criteria and translating model outputs into operational or commercial outcomes.

• Demonstrated ability to mentor data scientists, improve engineering standards and influence technical direction without relying solely on formal authority.

• Proven experience leading technical design, model review and delivery across multidisciplinary teams.

 

 

Technical Skills

Programming & Query Languages Python (pandas, PySpark), SQL (Hive, Trino)

Big Data & ML Platforms Databricks

Machine Learning K-Means/clustering, XGBoost, CatBoost, LightGBM, classification, regression, anomaly detection etc

MLOps & DevOps MLflow, CI/CD pipelines, Azure DevOps, model monitoring & drift detection

Visualisation & Reporting Power BI, Databricks

Behavioural & Core Competencies

 

• Strategic Thinking & Problem Solving - able to translate ambiguous business problems into structured analytical approaches.

• Analytical & Innovative - rigorous, curious, and constantly looking for better ways to model and interpret data.

• Communication & Business Storytelling - able to explain technical concepts and results in clear, business-friendly language for both technical and executive audiences.

• Cross-Functional Collaboration - comfortable partnering with business, engineering and governance stakeholders.

• Attention to Detail & Ownership - takes accountability for code quality, model accuracy and documentation.

• Adaptability & Resilience - thrives in a fast-paced environment spanning multiple concurrent projects and priorities.

• Technical Leadership & Judgement - sets direction, challenges weak assumptions and makes sound decisions under uncertainty.

• Coaching & Capability Building - develops others through practical guidance, constructive review and meaningful delegation.

• Commercial Acumen & Value Orientation - connects analytical work to measurable customer, revenue, cost, risk or experience outcomes.

• Governance & Risk Ownership - anticipates model, data and operational risks and ensures appropriate controls are built into delivery

Package & Remuneration

negotiable