Data Scientist – Machine Learning

Overview

In this role you will develop and operationalise machine learning solutions within NTT DATA UK’s Data Practice, enabling evidence-led decision making for clients. You will collaborate with data engineers, architects and consultants across Snowflake, Databricks and Microsoft Fabric to build production-ready models. You’ll guide the model lifecycle from data understanding to deployment and ongoing improvement, with a focus on responsible AI and explainability. This is a hands-on, impact-driven opportunity to shape analytics outcomes at scale.

Pay / Benefits

• flexible work options
• continuous growth and development opportunities
• tailored benefits
• inclusive culture
• opportunity to work across leading data platforms

Responsibilities

• Develop ML models for forecasting, classification, recommendation, optimisation, clustering and anomaly detection
• Apply statistical and ML techniques to structured and semi-structured data
• conduct exploratory data analysis, feature engineering, model training, validation and evaluation
• Compare model 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 or production environments
• Collaborate with consultants and client stakeholders to translate business problems into analytical tasks
• Frame ambiguous questions into testable hypotheses and measurable outcomes
• Communicate insights and recommendations clearly to non-technical audiences
• Support proof-of-concepts, project delivery and technical documentation
• Work with Data Engineering and Architecture teams to access, prepare and validate data for ML workloads
• Use platforms such as Snowflake, Databricks and Microsoft Fabric for data prep, experimentation and deployment support
• Follow engineering practices for version control, testing, documentation and reproducibility
• Contribute to MLOps, including model versioning, deployment handover and performance monitoring
• Consider explainability, data quality, bias, privacy and responsible AI implications

Key requirements

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

• clear communication
• collaboration
• business acumen
• Python
• SQL
• Scikit-Learn

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