Data Scientist, NTT DATA

• We are looking for a Data Scientist with a solid background in Machine Learning, Statistics and Predictive Analytics to join NTT DATA UK's Data Practice
• Experience with Generative / Agentic AI, LLMs and AI-enabled products is preferred, but the core of this role is strong applied machine learning capability rather than AI tooling alone
• 3-5 years of commercial experience in Data Science, Machine Learning or Advanced Analytics roles
• Strong hands-on Python experience for data analysis, feature engineering and model development
• Strong SQL skills and experience working with complex or sizeable datasets
• Solid theoretical and practical understanding of supervised learning, unsupervised learning, statistical modelling, feature engineering, validation, model evaluation and optimisation
• Hands-on experience with Python-based machine learning libraries, especially Scikit-Learn and at least one of XGBoost, LightGBM, TensorFlow or PyTorch
• Experience developing models beyond exploratory analysis into reusable, documented and testable assets, ideally with exposure to pilot or production environments
• Strong understanding of model evaluation, including appropriate metrics, validation approaches, overfitting, data leakage, baseline comparison and business impact assessment
• Ability to explain model assumptions, limitations, drivers and risks, including awareness of model explainability, bias, data quality and responsible AI considerations
• Clear communication skills and the ability to work collaboratively in mixed technical and business teams

Desirable

• You will join NTT DATA UK's Data Practice, a multi-disciplinary team delivering enterprise-scale data platforms, analytics and data-led transformation for clients
• As part of the growing Data Science capability within the Data Practice, you will work on Machine Learning, Predictive Analytics and Applied AI use cases that help clients move beyond traditional reporting towards evidence-led, AI-enabled decision making
• You will work alongside data engineers, data architects, platform specialists and consultants across Snowflake, Databricks and Microsoft Fabric to develop practical, production-ready machine learning solutions
• You will contribute hands-on across the model development lifecycle, from data understanding and feature engineering through to model training, evaluation, deployment support and ongoing improvement
• Develop machine learning models for forecasting, classification, recommendation, optimisation, clustering and anomaly detection
• Apply statistical and machine learning techniques to analyse structured and semi-structured datasets
• Perform exploratory data analysis, feature engineering, model training, validation and model evaluation
• Compare model approaches, select appropriate metrics and explain technical trade-offs clearly
• Build reusable notebooks, scripts and pipelines that support repeatable machine learning delivery
• Support monitoring and continuous improvement of models used in pilot or production environments
• Work with consultants and client stakeholders to understand business problems and translate them into analytical tasks
• Frame ambiguous questions into testable hypotheses, analytical approaches and measurable outcomes
• Communicate insights, model outputs and recommendations in a clear, practical and business-relevant way
• Support proof-of-concepts, project delivery, estimates, solution options and technical documentation where required
• Work with Data Engineering and Architecture teams to access, prepare and validate data for machine learning workloads
• Use platforms such as Snowflake, Databricks and Microsoft Fabric where appropriate for data preparation, experimentation and deployment support
• Follow good engineering practices for version control, testing, documentation and reproducibility
• Contribute to MLOps ways of working, including model versioning, deployment handover and performance monitoring
• Consider model explainability, data quality, bias, privacy and responsible AI implications where relevant