Data Scientist – Machine Learning

As part of NTT DATA UK's Data Practice, you will apply machine learning and predictive analytics to real business problems, delivering production-ready solutions in collaboration with engineers and consultants. You’ll work across data understanding, feature engineering, model training and deployment support to drive evidence-led, AI-enabled decision making for clients. The role blends ML model development with platform enablement on Snowflake, Databricks and Microsoft Fabric, focusing on practical impact. You will contribute to the end-to-end lifecycle and continuous improvement of models in pilot or production environments.

• flexible work options
• learning and development opportunities
• inclusive culture
• equal opportunities employer
• disability confident commitment

• Develop ML models for forecasting, classification, recommendation, optimisation, clustering and anomaly detection
• Apply statistical and ML techniques to structured and semi-structured data
• Perform EDA, feature engineering, model training, validation and evaluation
• Compare approaches, select metrics and explain trade-offs
• Build reusable notebooks, scripts and pipelines for repeatable ML delivery
• Support monitoring and continuous improvement of models in pilot/production environments
• Collaborate with data engineers and architects to access and validate data for ML workloads
• Contribute to MLOps practices including model versioning, deployment handover and monitoring
• Consider explainability, data quality, bias and responsible AI implications

• 3-5 years of commercial experience in Data Science, ML or Advanced Analytics
• Strong hands-on Python for data analysis and model development
• Strong SQL skills with large datasets
• Solid understanding of supervised/unsupervised learning, statistical modelling, feature engineering, validation, evaluation and optimization
• Hands-on experience with Python ML libraries (Scikit-Learn and at least one of XGBoost, LightGBM, TensorFlow or PyTorch)
• Experience turning models into reusable, documented, testable assets, with exposure to pilot or production environments
• Strong ability to explain model assumptions, drivers, risks and responsible AI considerations
• Clear communication and collaboration across technical and business teams

• Strong communication
• Team collaboration
• Curiosity and problem-solving
• Python (data analysis, feature engineering, modeling)
• SQL for complex datasets
• Scikit-Learn