Data Scientist – Machine Learning

Salary: £55,000 - 95,000 per year

Requirements:

• 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.
• Exposure to pilot or production deployment of machine learning models.
• Good understanding of MLOps concepts such as versioning, reproducibility, monitoring and deployment handover.
• Experience with at least one modern cloud data or analytics platform, such as Snowflake, Databricks, Microsoft Fabric, Azure, AWS or GCP.
• MSc or equivalent experience in Data Science, Statistics, Mathematics, Computer Science, Artificial Intelligence, Physics, Engineering or a related quantitative discipline.
• Experience with Spark or distributed data processing frameworks.
• Experience preparing data pipelines or analytical datasets to support machine learning workloads.
• Awareness of data engineering, data architecture, data governance and enterprise data platform concepts.

Responsibilities:

• 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.

Technologies:

• AI
• AWS
• Azure
• Cloud
• Databricks
• Fabric
• GCP
• Support
• Machine Learning
• MLOps
• Model Training
• Network
• PyTorch
• Python
• SQL
• Snowflake
• Spark
• TensorFlow
• Agentic AI

More:

We are NTT DATA UK&I, an ambitious and inclusive business with a growing Data Practice delivering enterprise-scale data platforms, analytics and data-led transformation for clients. You will join our multi-disciplinary team working across machine learning, predictive analytics and applied AI, alongside data engineers, data architects, platform specialists and consultants. We offer tailored benefits that support physical, emotional and financial wellbeing, continuous learning and development opportunities, and flexible work options. We are committed to creating a positive, equitable and rewarding place to work where people can thrive, with a strong focus on respect, teamwork, innovation and responsible innovation. Our inclusive culture is supported by several Inclusion Networks, including the Womens Business Network, Cultural and Ethnicity Network, LGBTQ+ & Allies Network, Neurodiversity Network and Parent Network. We are an equal opportunities employer and a Disability Confident Committed Employer.

last updated 34 week of 2026