Data Scientist – Machine Learning

Overview

In this role, you join the Data Practice to deliver production-ready machine learning solutions for clients, contributing across the full model lifecycle from data understanding to deployment and improvement. You will work with cross-functional teams on platforms like Snowflake, Databricks and Microsoft Fabric to enable evidence-based, AI-enabled decision making. The role emphasises applied ML, responsible AI, and real-world impact within an enterprise setting. This is a hands-on, collaborative position with a focus on scalable data-driven outcomes.

Pay / Benefits

• tailored benefits that support wellbeing and finances
• focus on continuous learning and development
• flexible work options

Responsibilities

• Develop ML models for forecasting, classification, recommendation, optimisation, clustering and anomaly detection
• Apply ML techniques to structured and semi-structured data
• Perform EDA, feature engineering, model training, validation and evaluation
• Build reusable notebooks, scripts and pipelines for repeatable ML delivery
• Support monitoring and continuous improvement of models in pilot or production environments
• Collaborate with consultants and clients to translate business problems into analytical tasks
• Frame ambiguous questions into testable hypotheses and measurable outcomes
• Communicate insights and recommendations in a business-friendly way
• Support PoCs, project delivery, estimates and technical documentation
• Work with Data Engineering/Architecture to access, prepare and validate data for ML workloads
• Utilise Snowflake, Databricks and Microsoft Fabric for data prep, experimentation and deployment support
• Follow good engineering practices for version control, testing, and reproducibility
• Contribute to MLOps practices including model versioning and deployment handover
• Consider explainability, data quality, bias, privacy and responsible AI where relevant

Key requirements

• 3-5 years of commercial experience in Data Science, ML or Advanced Analytics
• Strong Python experience for data analysis, feature engineering and model development
• Strong SQL skills with complex/large datasets
• Solid understanding of supervised/unsupervised learning, statistical modelling, validation and optimization
• Hands-on experience with Python ML libraries (Scikit-Learn and at least one of XGBoost, LightGBM, TensorFlow or PyTorch)
• Experience developing reusable, documented, testable ML assets for pilot/production environments
• Strong model evaluation knowledge including metrics, validation approaches, overfitting, data leakage, baseline comparison and business impact
• Ability to explain model assumptions, limitations, drivers, risks; awareness of explainability, bias, data quality and responsible AI
• Clear communication and ability to collaborate in mixed technical/business teams

• Clear communication
• Collaboration in cross-functional teams
• Analytical rigour
• Python for data analysis and ML
• SQL for large datasets
• Scikit-Learn

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