Senior Data Scientist

Requirements

• Strong analytical and problem-solving skills, with the ability to diagnose and address modelling maintenance issues.
• Solid technical expertise, including proficiency in Python (or equivalent scripting tools) for model updates, automation, and data validation.
• Hands-on experience in statistical modelling, model recalibration, and ongoing maintenance of production models.
• Strong understanding of data pipelines and production environments, with the ability to support PoS -based modelling processes (highly desirable).
• Experience in quality assurance, sample optimization, and ensuring stability and reliability of model outputs over time.
• Ability to monitor KPIs, detect anomalies, and perform root cause analysis to support continuous model improvement.
• Familiarity with end-to-end panel and modelling value chain, including extrapolation logic and data quality controls.
• Strong documentation skills, with the ability to clearly explain modelling approaches, updates, and assumptions.
• Proven experience working in international and cross-market environments (preferably with exposure to Pacific markets).
• Strong quality mindset, with a solution-driven approach to resolving modelling and data challenges.
• Excellent communication and organizational skills, enabling effective collaboration with technical and business stakeholders.
• Ability to work independently while contributing to a broader team setup.
• Strong command of English (written and verbal).
• Extensive experience within the Panel Quality and modelling value chain is a strong advantage.
• This role is ideal for individuals with a keen eye for detail and a passion for data science and quality assurance. If you enjoy working in a dynamic environment and have the skills listed above, we encourage you to apply for the Data Scientist position.

Benefits

Additional Information

Responsible for maintaining, recalibrating, and optimizing production models for Retail Panels, with a strong focus on model performance in Pacific markets. The role ensures model accuracy, stability, and compliance through KPI monitoring, quality controls, and continuous model improvements, supported by strong statistical and technical expertise in PoS-based production environments. Key Activities: Modelling Maintenance: Maintain, update, and recalibrate production of missing retail models, ensuring stability and accuracy over time. Develop and enhance models for missing retailers (both outside and within the system), including transition to Stat Ops for ongoing production maintenance. Support continuous model optimization, including parameter tuning, recalibration, and validation of extrapolation logic. Design and implement modelling setups for new country/channel expansions, including universe definitions and sampling frameworks. Ensure modelling frameworks are production-ready, scalable, and aligned with PoS/RMS data structures. 2. Quality Assurance (Modelling-Focused) Perform QA on model outputs, ensuring consistency, robustness, and compliance with T&D standards. Optimize samples and modelling inputs by analyzing shop contributions and identifying inefficiencies impacting model performance. Detect and resolve modelling-related outliers (MDQC), with clear impact assessment and end-to-end correction approach. Validate model changes and recalibrations through structured testing and impact analysis before deployment to production. 3. Quality Monitoring & Model Performance Monitor model performance KPIs (e.g., stability, variance, panel health indicators) and proactively identify risks. Conduct root cause analysis (RCA) for modelling issues, including anomalies in extrapolation, sample distortions, and missing retailer impact. Resolve Level 2 methodology and modelling-related queries, ensuring alignment with global standards. Define and implement continuous improvement actions to enhance model accuracy and reliability. 4. Methodology, Automation & Universe Management Design, manage, and update universe studies, ensuring alignment with evolving market conditions in Pacific. Translate universe study outputs into model updates, including implementation in production systems (e.g., POS Manager). Conduct simulations and scenario testing to validate extrapolation and modelling changes prior to rollout. Document modelling methodologies, updates, and assumptions to ensure transparency and reproducibility. Drive automation and standardization of modelling maintenance processes where possible.

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