Job Description
The Senior Data Scientist will be responsible for translating business problems into data-driven and AI-enabled solutions. The role requires strong expertise in data analysis, machine learning, data engineering, and stakeholder engagement, while working closely with data engineering, AI platform, and observability teams.

Key Responsibilities

• Translate business problems intodata-driven and AI-enabled solutions
• Perform exploratory data analysis touncover patterns, issues, and opportunities
• Design, build, and maintain datapipelines to support analytics and modelling use cases
• Develop, train, evaluate, and iterateon machine learning and AI models
• Apply appropriate model evaluationtechniques and define success metrics
• Support operational data workflowsand resolve day-to-day data processing issues when required
• Produce clear dashboards, reports,and visualisations for stakeholders
• Communicate insights, modelbehaviour, and recommendations to both technical and business audiences
• Collaborate closely with dataengineering, AI platform, and observability teams to productionisesolutions
• Contribute to best practices arounddata quality, governance, and responsible use of AI

• Excel, SQL, PowerBI, AWS and quicksight
• Data analysis, exploration, andfeature engineering (EDA)
• Strong applied statistics and machinelearning foundations
• Python-based data science and MLstack (e.g. pandas, NumPy, scikit-learn, PyTorch / TensorFlow)
• Data engineering skills: ETL design,batch and streaming data processing
• Experience with distributed datasystems (e.g. Kafka, Spark or equivalent)
• SQL and structured / semi-structureddata querying
• Experiment design, model evaluation,and validation techniques
• Dashboarding, reporting, and datavisualisation
• Business problem translation andrequirements understanding
• Version control and collaborativedevelopment (Git)

• MLOps practices (model packaging,deployment pipelines, monitoring awareness)
• Data governance principles (dataquality, lineage, ownership, compliance awareness)
• Model evaluation, performancetracking, and drift detection concepts
• Cloud-based data and ML environments(Azure / AWS)
• Generative AI and LLM-based solutionexperience
• AI agent or advanced promptingfamiliarity
• Experience collaborating withobservability and platform engineering teams
• Domain-specific knowledge aligned tobusiness use cases