3071 Data Scientist (Senior)

SUMMARY:
ESSENTIAL SKILLS:

• Data analysis, exploration, and feature engineering (EDA)
• Strong applied statistics and machine learning foundations
• Python-based data science and ML stack (e.g. pandas, NumPy, scikit-learn, PyTorch / TensorFlow)
• Data engineering skills: ETL design, batch and streaming data processing
• Experience with distributed data systems (e.g. Kafka, Spark or equivalent)
• SQL and structured / semi-structured data querying
• Experiment design, model evaluation, and validation techniques
• Dashboarding, reporting, and data visualisation for insights and decision support
• Business problem translation and requirements understanding
• Version control and collaborative development (Git)

• MLOps practices (model packaging, deployment pipelines, monitoring awareness)
• Data governance principles (data quality, lineage, ownership, compliance awareness)
• Model evaluation, performance tracking, and drift detection concepts
• Cloud-based data and ML environments (Azure / AWS)
• Generative AI and LLM-based solution experience (preferred, not essential)
• AI agent or advanced prompting familiarity
• Experience collaborating with observability and platform engineering teams
• Domain-specific knowledge aligned to business use cases

POSITION INFO:
ROLE & RESPONSIBILITIES:

• Translate business problems into data-driven and AI-enabled solutions
• Perform exploratory data analysis to uncover patterns, issues, and opportunities
• Design, build, and maintain data pipelines to support analytics and modelling use cases
• Develop, train, evaluate, and iterate on machine learning and AI models
• Apply appropriate model evaluation techniques and define success metrics
• Support operational data workflows and resolve day-to-day data processing issues when required
• Produce clear dashboards, reports, and visualisations for stakeholders
• Communicate insights, model behaviour, and recommendations to both technical and business audiences
• Collaborate closely with data engineering, AI platform, and observability teams to productionise solutions
• Contribute to best practices around data quality, governance, and responsible use of AI

• Is outcome-driven, not model-driven
• Is comfortable working close to data pipelines and production constraints
• Can explain models and uncertainty to business stakeholders
• Collaborates naturally with platform, AI engineering, and observability teams
• Understands that trust, transparency, and governance are as important as accuracy

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